[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-ThinamXx--300Days__MachineLearningDeepLearning":3,"tool-ThinamXx--300Days__MachineLearningDeepLearning":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",146793,2,"2026-04-08T23:32:35",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108111,"2026-04-08T11:23:26",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":78,"owner_website":79,"owner_url":80,"languages":78,"stars":81,"forks":82,"last_commit_at":83,"license":84,"difficulty_score":32,"env_os":85,"env_gpu":86,"env_ram":86,"env_deps":87,"category_tags":97,"github_topics":98,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":102,"updated_at":103,"faqs":104,"releases":105},5772,"ThinamXx\u002F300Days__MachineLearningDeepLearning","300Days__MachineLearningDeepLearning","I am sharing my Journey of 300DaysOfData in Machine Learning and Deep Learning.","300Days__MachineLearningDeepLearning 是一位开发者记录的\"300 天数据科学之旅”开源项目，旨在系统性地分享机器学习与深度学习的学习路径与实践成果。该项目解决了初学者在面对海量学习资源时容易迷失方向、缺乏系统性实战演练的痛点，通过整合经典教材、前沿论文与代码实现，提供了一条清晰的进阶路线。\n\n内容涵盖从基础的《机器学习实战》到深度的 PyTorch、Fastai 框架应用，并包含了大量动手项目，如房价预测、手写数字识别、风格迁移、情感分析及生成对抗网络（GAN）等。其独特亮点在于“学练结合”的模式：不仅列出了完成状态的书单和资源，还配套了完整的 Notebook 代码复现，让读者能直接运行并理解算法从理论到落地的全过程。\n\n这套资源非常适合希望系统入门或巩固基础的 AI 开发者、计算机专业学生及自学者使用。对于想要摆脱碎片化知识积累，希望通过结构化项目和经典案例深入理解模型原理的研究人员，300Days__MachineLearningDeepLearning 也是一份极具参考价值的实战指南。","# **Journey of 300DaysOfData in Machine Learning and Deep Learning**\n\n\n![MachineLearning](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_47648b0a0ffd.jpg)\n\n| Books and Resources | Status of Completion |\n| ----- | -----|\n| 1. [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html) | :white_check_mark: |\n| 2. **A Comprehensive Guide to Machine Learning** | :white_check_mark: |\n| 3. **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow** | :white_check_mark: |\n| 4. [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F) | |\n| 5. [**Machine Learning Crash Course**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course) | :white_check_mark: |\n| 6. [**Deep Learning with PyTorch: Part I**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch) | :white_check_mark: |\n| 7. [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002F) | :white_check_mark: |\n| 8. [**Logistic Regression Documentation**](https:\u002F\u002Fml-cheatsheet.readthedocs.io\u002Fen\u002Flatest\u002Flogistic_regression.html) | :white_check_mark: |\n| 9. **Deep Learning for Coders with Fastai and PyTorch** | :white_check_mark: |\n| 10. **Approaching Almost Any Machine Learning Problem** | |\n| 11. [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F) | |\n\n| Research Papers |\n| --------------- |\n| 1. [**Practical Recommendations for Gradient based Training of Deep Architectures**](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1206.5533.pdf) |\n\n| Projects and Notebooks |\n| ---------------------- |\n| 1. [**California Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git) |\n| 2. [**Logistic Regression from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLogisticRegression\u002FLogisticRegression.ipynb) |\n| 3. [**Implementation of LeNet Architecture**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb) |\n| 4. [**Neural Networks Style Transfer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER) |\n| 5. [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition) |\n| 6. [**Dog Breed Identification: ImageNet**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification) |\n| 7. [**Sentiment Analysis Dataset Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb) |\n| 8. [**Sentiment Analysis with RNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb) |\n| 9. [**Sentiment Analysis with CNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb) |\n| 10. [**Natural Language Inference Dataset**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb) |\n| 11. [**Natural Language Inference: Attention**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb) |\n| 12. [**Natural Language Inference: BERT**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb) |\n| 13. [**Deep Convolutional GAN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb) |\n| 14. [**Fastai: Introduction Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb) |\n| 15. [**Fastai: Image Detection**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb) |\n| 16. [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb) |\n| 17. [**Fastai: Image Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb) |\n| 18. [**Fastai: Multilabel Classification & Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb) |\n| 19. [**Fastai: Image Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FRegression.ipynb) |\n| 20. [**Fastai: Advanced Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F6.%20Advanced%20Classification\u002FImagenetteClassification.ipynb) |\n| 21. [**Fastai: Collaborative Filtering**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb) |\n| 22. [**Fastai: Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb) |\n| 23. [**Fastai: Natural Language Processing**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb) |\n| 24. [**Fastai: Data Munging**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb) |\n| 25. [**Fastai: Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb) |\n| 26. [**Fastai: Convolutional Neural Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb) |\n| 27. [**Fastai: Residual Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb) |\n| 28. [**Fastai: Architecture Details**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F14.%20Architecture%20Details\u002FArchitectures.ipynb) |\n| 29. [**Fastai: Training Process**](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7fa5a0d8854e.png) |\n| 30. [**Fastai: Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb) |\n| 31. [**Fastai: CNN Interpretation with CAM**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F17.%20CNN%20Interpretation\u002FCNN%20Interpretation.ipynb) |\n| 32. [**Fastai: Fastai Learner from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb) |\n| 33. [**Fastai: Chest X-Rays Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb) |\n| 34. [**Supervised and Unsupervised Learning**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb) |\n| 35. [**Evaluation Metrics**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb) |\n| 36. [**OpenCV Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOpenCV.ipynb) |\n| 37. [**OpenCV Project I**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20I.ipynb) | \n| 38. [**OpenCV Project II**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20II.ipynb) |\n| 39. [**Convolution**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetwork\u002FConvolutions.ipynb) |\n| 40. [**Convolutional Layers**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb) |\n| 41. [**Fastai: Transformers**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F20.%20Transformers\u002FTransformers.ipynb) |\n\n**Day1 of 300DaysOfData!**\n- **Gradient Descent and Cross Validation**: Gradient Descent is an iterative approach to approximating the Parameters that minimize a Differentiable Loss Function. Cross Validation is a resampling procedure used to evaluate Machine Learning Models on a limited Data sample which has a parameter that splits the data into number of groups. On my Journey of Machine Learning and Deep Learning, Today I have read in brief about the fundamental Topics such as Calculus, Matrices, Matrix Calculus, Random Variables, Density Functions, Distributions, Independence, Maximum Likelihood Estimation and Conditional Probability. I have also read and Implemented about Gradient Descent and Cross Validation. I am starting this Journey from Scratch and I am following the Book:**Machine Learning From Scratch**. I have presented the Implementation of Gradient Descent and Cross Validation here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above. I am excited about the days to come!!\n- Book:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d6613ffd52f6.png)\n\n**Day2 of 300DaysOfData!**\n- **Ordinary Linear Regression**: Linear Regression is a linear approach to modelling the relationships between a scalar response or dependent variable and one or more explanatory variables or independent variables. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ordinary Linear Regression, Parameter Estimation, Minimizing Loss and Maximizing Likelihood along with the Construction and Implementation of the LR from the Book **Machine Learning From Scratch**. I have also started reading the Book **A Comprehensive Guide to Machine Learning** which focuses on Mathematics and Theory behind the Topics. I have read about Regression, Ordinary Least Squares, Vector Calculus, Orthogonal Projection, Ridge Regression, Feature Engineering, Fitting Ellipses, Polynomial Features, Hyperparameters and Validation, Errors and Cross Validation from this book. I have presented the Implementation of Linear Regression along with Visualizations using Python here in the Snapshots. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!\n- Books:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **A Comprehensive Guide to Machine Learning**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a43d6dbcc656.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f36061adb17c.png)\n\n**Day3 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Regularized Regression such as Ridge Regression and Lasso Regression, Bayesian Regression, GLMs, Poisson Regression along with Construction and Implementation of the same from the Book **Machine Learning From Scratch**. I have also read the Book **A Comprehensive Guide to Machine Learning** which focuses on Mathematics and Theory behind the Topics. I have read about Maximum Likelihood Estimation or MLE and Maximum a Posteriori or MAE for Regression, Probabilistic Model, Bias Variance Tradeoff, Metrics, Bias Variance Decomposition, Alternative Decomposition, Multivariate Gaussians, Estimating Gaussians from Data, Weighted Least Squares, Ridge Regression, and Generalized Least Squares from this Book. I have presented the Implementation of Ridge Regression, Lasso Regression along with Cross Validation, Bayesian Regression and Poisson Regression using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!\n- Books:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **A Comprehensive Guide to Machine Learning**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2bc03f880b51.png)\n\n**Day4 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Discriminative Classifiers such as Binary and Multiclass Logistic Regression, The Perceptron Algorithm, Parameter Estimation, Fishers Linear Discriminant and Fisher Criterion along with Construction and Implementation of the same from the Book **Machine Learning From Scratch**. I have also read the Book **A Comprehensive Guide to Machine Learning** which focuses on Mathematics and Theory behind the Topics. I have read about Kernels and Ridge Regression, Linear Algebra Derivation, Computational Analysis, Sparse Least Squares, Orthogonal Matching Pursuit, Total Least Squares, Low rank Formulation, Dimensionality Reduction, Principal Component Analysis, Projection, Changing Coordinates, Minimizing Reconstruction Errors and Probabilistic PCA from this Book. I have presented the Implementation of Binary and Multiclass Logistic Regression, The Perceptron Algorithm and Fishers Linear Discriminant using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!\n- Books:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **A Comprehensive Guide to Machine Learning**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_32b738933069.png)\n\n**Day5 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Generative Classifiers such as Linear Discriminative Analysis or LDA, Quadratic Discriminative Analysis or QDA, Naive Bayes, Parameter Estimation and Data Likelihood along with Construction and Implementation of the same from the Book **Machine Learning From Scratch**. I have also read the Book **A Comprehensive Guide to Machine Learning** which focuses on Mathematics and Theory behind the Topics. I have read about Generative and Discriminative Classification, Bayes Decision Rule, Least Squares Support Vector Machines, Feature Extension, Neural Network Extension, Binary and Multiclass Logistic Regression, Loss Function, Training, Multiclass Extension, Gaussian Discriminant Analysis, QDA and LDA Classification and Support Vector Machines from this Book. I have presented the Implementation of LDA, QDA and Naive Bayes along with Visualizations using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!\n- Books:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **A Comprehensive Guide to Machine Learning**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2aa78b5d1c1c.png)\n\n**Day6 of 300DaysOfData!**\n- **Decision Trees**: A Decision Tree is an interpretable machine learning for Regression and Classification. It is a flow chart like structure in which each internal node represents a Test on an attribute and each branch represents the outcome of the Test. On my Journey of Machine Learning and Deep Learning, Today I have read about Decision Trees such as Regression Trees and Classification Trees, Building Trees, Making Splits and Predictions, Hyperparameters, Pruning and Regularization along with Construction and Implementation of the same from the Book **Machine Learning From Scratch**. I have also read the Book **A Comprehensive Guide to Machine Learning** which focuses on Mathematics and Theory behind the Topics. I have read about Decision Tree Learning, Entropy and Information, Gini Impurity, Stopping Criteria, Random Forests, Boosting and AdaBoost, Gradient Boosting and KMeans Clustering from this Book. I have presented the Implementation of Regression Trees and Classification Trees using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead!!\n- Books:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **A Comprehensive Guide to Machine Learning**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8bf971cffffb.png)\n\n**Day7 of 300DaysOfData!**\n- **Tree Ensemble Methods**: Ensemble Methods combine the outputs of multiple simple Models which is often called Learners in order to create the fine Model with low variance. Due to their high variance, a decision trees often fail to reach a level of precision comparable to other predictive algorithms and Ensemble Methods minimize the variance. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Tree Ensemble Methods such as Bagging for Decision Trees, Bootstrapping, Random Forests and Procedure, Boosting, AdaBoost for Binary Classification, Weighted Classification Trees, The Discrete AdaBoost Algorithm and AdaBoost for Regression along with Construction and Implementation of the same from the Book **Machine Learning From Scratch**. I have presented the Implementation of Bagging, Random Forests and AdaBoost along with different base estimators using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead !!\n- Books:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1f72600d1c96.png)\n\n**Day8 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Neural Networks from the Book **Machine Learning From Scratch**. I have read about Model Structure, Communication between Layers, Activation Functions such as ReLU, Sigmoid, The Linear Activation Function, Optimization, Back Propagation, Calculating Gradients, Chain Rule and Observations, Loss Functions along with Construction using The Loop Approach and The Matrix Approach and Implementation of the same from this Book. I have also read the Book **A Comprehensive Guide to Machine Learning** which focuses on Mathematics and Theory behind the Topics. I have read about Convolutional Neural Networks and Layers, Pooling Layers, Back Propagation for CNN, ResNet and Visual Understanding of CNNs from this Book. Besides, I have seen a couple of videos of Neural Networks and Deep Learning. I have presented the simple Implementation of Neural Networks with The Functional API and The Sequential API using TensorFlow here in the Snapshot. I hope you will also spend some time reading the Topics and Books mentioned above. Excited about the days ahead !!\n- Books:\n  - [**Machine Learning From Scratch**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **A Comprehensive Guide to Machine Learning**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_00dca87ea7ff.png)\n\n**Day9 of 300DaysOfData!**\n- **Reinforcement Learning**: In Reinforcement Learning, The Learning system called an agent in a particular context can observe the environment, select and perform actions and get rewards in return or penalties in the form of negative rewards. It must learn by itself what is the best policy to get the most reward over time. On my Journey of Machine Learning and Deep Learning, Today I have started reading and Implementing from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have read briefly about The Machine Learning Landscape viz. Types of Machine Learning Systems such as Supervised and Unsupervised Learning, Semisupervised Learning, Reinforcement Learning, Batch Learning and Online Learning, Instance Based Learning and Model Based Learning from this Book. I have presented the simple Implementation of Linear Regression and KNearest Neighbors along with a simple plot using Python here in the Snapshot. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2980f7cd451f.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_be833d8d9e75.png)\n\n**Day10 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read about the Main Challenges of Machine Learning such as Insufficient Quantity of Training Data, Non representative Training Data, Poor Quality Data, Irrelevant Features, Overfitting and Underfitting the Training Data and Testing and Validating, Hyperparameter Tuning and Model Selection and Data Mismatch from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have started working on **California Housing Prices** Dataset which is included in this Book. I will build a Model of Housing Prices in California in this Project. I have presented the simple Implementation of Data Processing and few techniques of EDA using Python here in the Snapshot. I have also presented the Implementation of Sweetviz Library for Analysis here. I really appreciate Chanin Nantasenamat for sharing about this Library in one of his videos. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n- [**Chanin Nantasenamat Video on Sweetviz**](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UR_OK8vBpeY&lc=z22itptbrzv0vfky504t1aokgq4l23pa5kermfzdyrfkbk0h00410.1605764911555430)\n- [**California Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4d093990331c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_daf1d3e8afae.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_30cc9b31daf9.png)\n\n**Day11 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Creating categories from attributes, Stratified Sampling, Visualizing Data to gain insights, Scatter Plots, Correlations, Scatter Matrix and Attribute Combinations from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have continued working with **California Housing Prices** Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I will build a Model of Housing Prices in California in this Project. I am still working on the same. I have presented the Implementation of Stratified Sampling, Correlations using Scatter Matrix and Attribute combinations using Python here in the Snapshots. I have also presented the Snapshots of Correlations using Scatter plots here. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead !! \n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n- [**California Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_efcb602aa8be.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_14e0220ad620.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c81581a794dc.png)\n\n**Day12 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Preparing the Data for Machine Learning Algorithms, Data Cleaning, Simple Imputer, Ordinal Encoder, OneHot Encoder, Feature Scaling, Transformation Pipeline, Standard Scaler, Column Transformer, Linear Regression, Decision Tree Regressor and Cross Validation from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have continued working with **California Housing Prices** Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I will build a Model of Housing Prices in California in this Project. The Notebook contains almost every Topics mentioned above. I have presented the Implementation of Data Preparation, Handling missing values, OneHot Encoder, Column Transformer, Linear Regression, Decision Tree Regressor along with Cross Validation using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead !!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n- [**California Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_974b8e02957b.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b1bada89ff4d.png)\n\n**Day13 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have learned and Implemented about Random Forest Regressor, Ensemble Learning, Tuning the Model, Grid Search, Randomized Search, Analyzing the Best Models and Errors, Model Evaluation, Cross Validation and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have completed working with **California Housing Prices** Dataset which is included in this Book. This Dataset was based on Data from the 1990 California Census. I have built a Model using Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California. I have presented the Implementation of Random Forest Regressor and Tuning the Model with Grid Search and Randomized Search along with Cross Validation using Python here in the Snapshot. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!! \n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n- [**California Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c4d1f124a88a.png)\n\n**Day14 of 300DaysOfData!**\n- **Confusion Matrix**: Confusion Matrix is a better way to evaluate the performance of a Classifier. The general idea of Confusion Matrix is to count the number of times instances of Class A are classified as Class B. This approach requires to have a set of predictions so that they can be compared to the actual targets. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Classification, Training a Binary Classifier using Stochastic Gradient Descent, Measuring Accuracy using Cross Validation, Implementation of CV, Confusion Matrix, Precision and Recall and their Curves and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of SGD Classifier in MNIST Dataset along with Precision and Recall using Python here in the Snapshots. I have also presented the curves of Precision and Recall here. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. I am excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f84241932f38.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_70ce33a06385.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f8868541c0ef.png)\n\n**Day15 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about The ROC Curve, Random Forest Classifier, SGD Classifier, Multi Class Classification, One vs One and One vs All Strategies, Cross Validation, Error Analysis using Confusion Matrix, Multi Class Classification, KNeighbors Classifier, Multi Output Classification, Noises, Precision and Recall Tradeoff and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have completed the Topic Classification from this Book. I have presented the Implementation of The ROC Curve, Random Forest Classifier in Multi Class Classification, The One vs One Strategy, Standard Scaler, Error Analysis, Multi Label Classification and Multi Output Classification using Scikit Learn here in the Snapshots. I hope you will also work on the same. I hope you will also spend some time reading the Topics and Book mentioned above. I am excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ac5fe1d24fd1.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_abcc18e1a26a.png)\n\n**Day16 of 300DaysOfData!**\n- **Ridge Regression**: Ridge Regression is a regularized Linear Regression viz. a regularization term is added to the cost function which forces the learning algorithm to not only fit the Data but also keep the model weights as small as possible. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Training the Models, Linear Regression, The Normal Equations and Computational Complexity, Cost Function and Gradient Descent such as Batch Gradient Descent, Convergence Rate, Stochastic Gradient Descent, Mini batch Gradient Descent, Polynomial Regression and Poly Features, Learning Curves, Bias and Variance Tradeoff, Regularized Linear Models such as Ridge Regression and few more related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Polynomial Regression, Learning Curves and Ridge Regression along with Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_74135584226e.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d0eef550c50c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6869ef08dc6c.png)\n\n**Day17 of 300DaysOfData!**\n- **Elastic Net**: Elastic Net is a middle grouped between Ridge Regression and Lasso Regression. The regularization term **r** is a simple mix of both Ridge and Lasso's regularization terms. When **r** equals 0, it is equivalent to Ridge and when **r** equals 1, it is equivalent to Lasso Regression. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Lasso Regression, Elastic Net, Early Stopping, SGD Regressor, Logistic Regression, Estimating Probabilities, Training and Cost Function, Sigmoid Function, Decision Boundaries, Softmax Regression or Multinomial Logistic Regression, Cross Entropy and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have just started reading the Topic Support Vector Machines. I have presented the simple Implementation of Lasso Regression, Elastic Net, Early Stopping, Logistic Regression and Softmax Regression using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8f9360aab3bf.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e6fb40994b58.png)\n\n**Day18 of 300DaysOfData!**\n- **Support Vector Machines**: A Support Vector Machines or SVM is a very powerful and versatile Machine Learning model which is capable of performing Linear and Nonlinear Classification, Regression and even outlier detection. SVMs are particularly well suited for classification of complex but medium sized datasets. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Support Vector Machines, Linear SVM Classification, Soft Margin Classification, Nonlinear SVM Classification, Polynomial Regression, Polynomial Kernel, Adding Similarity Features, Gaussian RBF Kernel, Computational Complexity, SVM Regression which is Linear as well Nonlinear and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Nonlinear SVM Classification using SVC and Linear SVC along with Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2d7ca6f64985.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c6c928e6ab64.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c6e9f1f561f8.png)\n\n**Day19 of 300DaysOfData!**\n- **Voting Classifiers**: Voting Classifiers are the classifiers which aggregates the predictions of different Classifiers and predicts the class that gets the most votes. The majority vote classifier is called a Hard Voting Classifier. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ensemble Learning and Random Forests, Voting Classifiers such as Hard Voting and Soft Voting Classifiers and few more topics related to the same. Actually, I have also started working on a Research Project with an amazing Team. I have presented the Implementation of Hard Voting and Soft Voting Classifiers using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1fe45564bdaf.png)\n\n**Day20 of 300DaysOfData!**\n- **The CART Training Algorithm**: The Algorithm which represents Scikit Learn's Implementation of the Classification and Regression Tree or CART Training algorithm to train Decision Trees also called Growing Trees. It's working principle is splitting the Training set into two subsets using a feature and a threshold. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Decision Functions and Predictions, Decision Trees, Decision Tree Classifier, Making Predictions, Gini Impurity, White Box Models and Black Box Models, Estimating Class Probabilities, The CART Training Algorithm, Computational Complexities, Entropy, Regularization Hyperparameters, Decision Tree Regressor, Cost Function and Instability from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the simple Implementation of Decision Tree Classifier and Decision Tree Regressor along with Visualization of the same using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_954becc80ea8.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_863f97343bac.png)\n\n**Day21 of 300DaysOfData!**\n- **Bagging and Pasting**: It refers to the approach which uses the same Training Algorithm for every predictor but to train them on different random subsets of the Training set. When sampling is performed with replacement, it is called Bagging and when sampling is performed without replacement, it is called Pasting. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Ensemble Learning and Random Forests, Voting Classifiers, Bagging and Pasting in Scikit Learn, Out of Bag Evaluation, Random Patches and Random Subspaces, Random Forests, Extremely Randomized Trees Ensemble, Feature Importance, Boosting, AdaBoost, Gradient Boosting and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Bagging Ensembles, Decision Trees, Random Forest Classifier, Feature Importance, AdaBoost Classifier and Gradient Boosting using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ca918300ee73.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e49b706659b4.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_239a06cb4126.png)\n\n**Day22 of 300DaysOfData!**\n- **Manifold Learning**: Manifold Learning refers to the Dimensionality Reduction Algorithms that work by modeling the manifold on which the training instances lie which relies on manifold hypothesis which holds that most real world high dimensional datasets to a much lower dimensional manifold. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gradient Boosting, Early Stopping, Stochastic Gradient Boosting, Extreme Gradient Boosting or XGBoost, Stacking and Blending, Dimensionality Reduction, Curse of Dimensionality, Approaches for Dimensionality Reduction, Projection and Manifold Learning and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Gradient Boosting with Early Stopping along with Visualization using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fb432add10e8.png)\n\n**Day23 of 300DaysOfData!**\n- **Incremental PCA**: Incremental PCA or IPCA Algorithms are the algorithms in which we can split the Training set into mini batches and feed an IPCA Algorithm one mini batch at a time. It is useful for large Training sets and also to apply PCA online. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Principal Component Analysis or PCA, Preserving the Variance, Principal Components, Projecting Down the Dimensions, Explained Variance Ratio, Choosing the Right Number of Dimensions, PCA for Compression and Decompression, Reconstruction Error, Randomized PCA, SVD, Incremental PCA and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of PCA, Randomized PCA and Incremental PCA along with Visualizations using Scikit Learn here in the Snapshots. I hope you will spend some time working on the same. I hope you will also spend some time reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_956d2524e7a9.png)\n\n**Day24 of 300DaysOfData!**\n- **Clustering**: Clustering Algorithms are the algorithms whose goal is to group similar instances together into Clusters. It is a great tool for Data Analysis, Customer Segmentation, Recommender Systems, Search Engines, Image Segmentation, Dimensionality Reduction and many more. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Kernel Principal Component Analysis, Selecting a Kernel and Tuning Hyperparameters, Pipeline and Grid Search, Locally Linear Embedding, Dimensionality Reduction Techniques such as Multi Dimensional Scaling, Isomap and Linear Discriminant Analysis, Unsupervised Learning such as Clustering and KMeans Clustering Algorithm and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Kernel PCA and Grid Search CV, and KMeans Clustering Algorithm along with a Visualization using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c5ecb84c4ff2.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b49645227ab4.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d3700add91f5.png)\n\n**Day25 of 300DaysOfData!**\n- **Image Segmentation**: Image Segmentation is the task of partitioning an Image into multiple segments. In Semantic Segmentation, all the pixels that are part of the same object type get assigned to the same segment. In Instance Segmentation, all pixels that are part of the individual object are assigned to the same segment. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about KMeans Algorithms, Centroid Initialization, Accelerated KMeans and Mini Batch KMeans, Finding the Optimal Numbers of Clusters, Elbow rule and Silhouette Coefficient score, Limitations of KMeans, Using Clustering for Image Segmentation and Preprocessing such as Dimensionality Reduction and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Clustering Algorithms for Image Segmentation and Preprocessing along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_68f4edcd368f.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_835d7f1d699c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ceafa3d7b88.png)\n\n**Day26 of 300DaysOfData!**\n- **Gaussian Mixtures Model**: A Gaussian Mixture Model or GMM is a probabilistic Model that assumes that the instances were generated from the mixture of several Gaussian distributions whose parameters are unknown. All the instances generated from a single Gaussian Distributions form a cluster that typically looks like an Ellipsoid. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about using Clustering Algorithms for Semi Supervised Learning, Active Learning and Uncertainty Sampling, DBSCAN, Agglomerative Clustering, Birch Algorithms, Mean Shift and Affinity Propagation Algorithms, Spectral Clustering, Gaussian Mixtures Model, Expectation Maximization Algorithm and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Clustering Algorithms for Semi supervised Learning and DBSCAN along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_77f898c5d789.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_357d5fb16491.png)\n\n**Day27 of 300DaysOfData!**\n- **Anomaly Detection**: Anomaly Detection also called Outlier Detection is the task of detecting instances that deviate strongly from the norm. These instances are called anomalies or outliers while the normal instances are called inliers. It is useful in Fraud Detection and more. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gaussian Mixture Models, Anomaly Detection using Gaussian Mixtures, Novelty Detection, Selecting the Number of Clusters, Bayesian Information Criterion, Akaike Information Criterion, Likelihood Function, Bayesian Gaussian Mixture Models, Fast MCD, Isolation Forest, Local Outlier Factor, One Class SVM and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have just started Neural Networks and Deep Learning from this Book. I have presented the Implementation of Gaussian Mixture Model along with Visualizations using Python here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d462154b926e.png)\n\n**Day28 of 300DaysOfData!**\n- **Rectified Linear Unit Function or ReLU** : It is a continuous but not differentiable at 0 where the slope changes abruptly and makes the Gradient Descent bounce around. It works very well and has the advantage of fast to compute. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Introduction to Artificial Neural Networks with Keras, Biological Neurons, Logical Computations with Neurons, The Perceptron, Hebbian Learning, Multi Layer Perceptron and Backpropagation, Gradient Descent, Hyperbolic Tangent Function and Rectified Linear Unit Function, Regression MLPs, Classification MLPs, Softmax Activation and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Building an Image Classifier using the Sequential API along with Visualization using Keras here in the Snapshots. I hope you will spend some time working on the same and reading the Topics and Book mentioned above. I am excited about the days ahead !!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b7796ddb42f5.png)\n\n**Day29 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Creating the Model using Sequential API, Compiling the Model, Loss Function and Activation Function, Training and Evaluating the Model, Learning Curves, Using the Model to make Predictions, Building the Regression MLP using the Sequential API, Building Complex Models using the Functional API, Deep Neural Networks and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Building Regression MLP using Sequential API and Functional API here in the Snapshots. I hope you will gain some insights and you will spend some time working on the same. I hope you will also spend some time reading and Implementing the Topics from the Book mentioned above. I am excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9eb094d5a2ea.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ce434d3cbc9f.png)\n\n**Day30 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Building the Complex Models using Functional API, Deep Neural Network Architecture, ReLU Activation Function, Handling Multiple Inputs in the Model, Mean Squared Error Loss Function and Stochastic Gradient Descent Optimizer, Handling Multiple Outputs or Auxiliary Output for Regularization and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Handling Multiple Inputs using Keras Functional API along with the Implementation of Handling Multiple Outputs or Auxiliary Output for Regularization using the same here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. I am excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8f638beb5a1e.png)\n\n**Day31 of 300DaysOfData!**\n- **Callbacks and Early Stopping**: Early Stopping is a method that allows you to specify an arbitrarily large number of Training epochs and stopping once the Model stops improving on the validation dataset. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Building Dynamic Models using the Sub Classing API, Sequential API and Functional API, Saving and Restoring the Model, Using Callbacks, Model Checkpoints, Early Stopping, Weights and Biases and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Building Dynamic Models using the Sub Classing API along with the Implementation of Using Callbacks and Early Stopping here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead!!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bf4978ac651c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_19d0c6663fcd.png)\n\n**Day32 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Visualization using TensorBoard, Learning Curves, Fine Tuning Neural Network Hyperparameters, Randomized Search CV, Regressor, Libraries to optimize Hyperparameters such as Hyperopt, Talos and few more, Number of Hidden Layers, Number of Neurons per Hidden Layer, Learning Rate, Batch size and Other Hyperparameters and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have also spend some time reading the Paper which is named as **Practical Recommendations for Gradient based Training of Deep Architectures**. Here, I have read about Deep Learning and Greedy Layer Wise Pretraining, Online Learning and Optimization of Generalization Error and few more related to the same. I have presented the Implementation of Tuning Hyperparameters, Keras Regressors and Randomized Search CV here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n- Paper:\n  - [**Practical Recommendations for Gradient based Training of Deep Architectures**](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1206.5533.pdf)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f5eaf5fa6e77.png)\n\n**Day33 of 300DaysOfData!**\n- **Vanishing Gradient**: During Backpropagation and calculating Gradients, it often gets smaller and smaller as the Algorithms progresses down to the lower layers which prevents the Training to converge to the good solution. This leads to Vanishing Gradient Problem. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Training Deep Neural Networks, Vanishing and Exploding Gradient Problems, Glorot and He Initialization, Non Saturating Activation Functions, Batch Normalization and its Implementation, Logistic and Sigmoid Activation Function, SELU Activation Function, ReLU Activation Function and Variants, Leaky ReLU and Parametric Leaky ReLU and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Leaky ReLU and Batch Normalization here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6ec626fe9e93.png)\n\n**Day34 of 300DaysOfData!**\n- **Gradient Clipping**: Gradient Clipping is the Technique to lessen the exploding Gradients problem which simply clip the Gradients during backpropagation so that they never exceed some threshold and it is mostly used in Recurrent Neural Networks. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Gradient Clipping, Batch Normalization, Reusing Pretrained Layers, Deep Neural Networks and Transfer Learning, Unsupervised Pretraining, Restricted Boltzmann Machines, Pretraining on an Auxiliary Task, Self Supervised Learning, Faster Optimizers, Gradient Descent Optimizer, Momentum Optimization, Nesterov Accelerated Gradient and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the simple Implementation of Transfer Learning using Keras and Sequential API here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0883dbea8d95.png)\n\n**Day35 of 300DaysOfData!**\n- **Adam Optimization**: Adam which stands for Adaptive Moment Estimation combines the ideas of Momentum Optimization and RMSProp where Momentum Optimization keeps track of an exponentially decaying average of past gradients and RMSProp keeps track of an exponentially decaying average of past squared gradients. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about AdaGrad Algorithm, Gradient Descent, RMSProp Algorithm, Adaptive Moment Estimation or Adam Optimization, Adamax, Nadam Optimization, Training Sparse Models, Dual Averaging, Learning Rate Scheduling, Power Scheduling, Exponential Scheduling, Piecewise Constant Scheduling, Performance Scheduling and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Exponential Scheduling and Piecewise Constant Scheduling here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5e3f67349315.png) \n\n**Day36 of 300DaysOfData!**\n- **Deep Neural Networks**: The best Deep Neural Networks configurations which will work fine in most cases without requiring much Hyperparameter Tuning is here: Kernel Initializer as LeCun Initialization, Activation Function as SELU, Normalization as None, Regularization as Early Stopping, Optimizer as Nadam, Learning Rate Schedule as Performance Scheduling. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Avoiding Overfitting Through Regularization, L1 and L2 Regularization, Dropout Regularization, Self Normalization, Batch Normalization, Monte Carlo Dropout, Max Norm Regularization, Activation Functions like SELU and Leaky ReLU, Nadam Optimization and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of L2 Regularization and Dropout Regularization using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_42473506a7b7.png)\n\n**Day37 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Custom Models and Training with TensorFlow, High Level Deep Learning APIs, IO and Preprocessing, Lower Level Deep Learning APIs, Deployment and Optimization, TensorFlow Architecture, Tensors and Operations, Keras Low Level API, Tensors and Numpy, Sparse Tensors, Arrays, String Tensors, Custom Loss Functions, Saving and Loading the Models containing Custom Components and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have also started reading a Book **Speech and Language Processing**. Here, I have read about Regular Expressions, Text Normalization, Tokenization, Lemmatization, Stemming, Sentence Segmentation, Edit Distance and few more Topics related to the same. I have presented the simple Implementation of Custom Loss Function here in the Snapshot. I hope you will also spend some time reading the Topics from the Books mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7a0411dcb0c1.png)\n\n**Day38 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Custom Activation Functions, Initializers, Regularizers and Constraints, Custom Metrics, MAE and MSE, Streaming Metric, Custom Layers, Custom Models, Losses and Metrics based on Models Internals and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have also started reading a Book **Speech and Language Processing**. Here, I have read about Regular Expressions, Basic Regular Expression Patterns, Disjunction, Range, Kleene Star, Wildcard Expression, Grouping and Precedence, Operator Hierarchy, Greedy and Non Greedy matching, Sequence and Anchors, Counters and few more Topics related to the same. I have presented the Implementation of Custom Activation Functions, Initializers, Regularizers, Constraints and Custom Metrics here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_db08b8aa4203.png)\n\n**Day39 of 300DaysOfData!**\n- **Prefetching and Data API**: Prefetching is the loading of the resource before it is required to decrease the time waiting for that resource. In other words, while the Training Algorithm is working on one batch the dataset will already be working in parallel on getting the next batch ready which will improve the performance dramatically. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Loading and Preprocessing Data using TensorFlow, The Data API, Chaining Transformations, Shuffling the Dataset, Gradient Descent, Interleaving Lines From Multiple Files, Parallelism, Preprocessing the Dataset, Decoding, Prefetching, Multithreading and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the simple Implementation of Data API using TensorFlow here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_255691cdb16c.png)\n\n**Day40 of 300DaysOfData!**\n- **Embedding and Representation Learning**: An Embedding is a trainable dense vector that represents a category. The better the representation of the categories, the easier it will be for the Neural Network to make accurate predictions, so Embeddings must make the useful representations of the categories. This is called Representation Learning. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about The Features API, Column Transformer, Numerical and Categorical Features, Crossed Categorical Features, Encoding Categorical Features using One Hot Vectors and Embeddings, Representation Learning, Word Embeddings, Using Feature Columns for Parsing, Using Feature Columns in the Models and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the simple Implementation of The Features API in Numerical and Categorical Columns along with Parsing here in the Snapshots. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fc0c1c2de986.png)\n\n**Day41 of 300DaysOfData!**\n- **Convolutional Layer**:The most important building block of CNN is the Convolutional Layer. Neurons in the first Convolutional Layer are not connected to every single pixel in the Input Image but only to pixels in their respective fields. Similarly, each Neurons in second CL is connected only to neurons located within a small rectangle in the first layer. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Deep Computer Vision using Convolutional Neural Networks, The Architecture of the Visual Cortex, Convolutional Layer, Zero Padding, Filters, Stacking Multiple Feature Maps, Padding, Memory Requirements, Pooling Layer, Invariance, Convolutional Neural Network Architectures and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the simple Implementation of Convolutional Neural Network Architecture here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_293764aa432e.png)\n\n**Day42 of 300DaysOfData!**\n- **ResNet Model**: Residual Network or ResNet won the ILSVRC 2015 Challenge, developed by Kaiming He, using an extremely deep CNN composed of 152 Layers. This Network uses the Skip connections which is also called Shortcut connections: The signal feeding into a layer is also added to the output of a layer located a bit higher up the stack. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about LeNet-5 Architecture, AlexNet CNN Architecture, Data Augmentation, Local Response Normalization, GoogLeNet Architecture, Inception Module, VGGNet, Residual Network or ResNet, Residual Learning, Xception or Extreme Inception, Squeeze and Excitation Network or SENet and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of ResNet 34 CNN using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e88d2ac6b72b.png)\n\n**Day43 of 300DaysOfData!**\n- **Xception Model**: Xception which stands for Extreme Inception is a variant of GoogLeNet Architecture which was proposed in 2016 by François Chollet. It merges the ideas of GoogLeNet and ResNet Architecture but it replaces the Inception modules with a special type of layer called a Depthwise Separable Convolution. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Using Pretrained Models from Keras, GoogLeNet and Residual Network or ResNet, ImageNet, Pretrained Models for Transfer Learning, Xception Model, Convolutional Neural Network, Batching, Prefetching, Global Average Pooling and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have presented the Implementation of Pretrained Models such as ResNet and Xception for Transfer Learning here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b1c85638d98d.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f34eb6c5db76.png)\n\n**Day44 of 300DaysOfData!**\n- **Semantic Segmentation**: In Semantic Segmentation, each pixel is classified according to the class of the object it belongs to but the different objects of the same class are not distinguished. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented about Classification and Localization, Crowdsourcing in Computer Vision, Intersection Over Union metric, Object Detection, Fully Convolutional Networks or FCNs, VALID Padding, You Only Look Once or YOLO Architecture, Mean Average Precision or MAP, Convolutional Neural Networks, Semantic Segmentation and few more Topics related to the same from the Book **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**. I have just completed learning from this Book. I have presented the Implementation of Classification and Localization along with the Visualization here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time reading the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Books:\n  - **Hands On Machine Learning with Scikit Learn, Keras and TensorFlow**\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_93e80d5a5d0c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_363a3606e74b.png)\n\n**Day45 of 300DaysOfData!**\n- **Empirical Risk Minimization**: Training a Model means learning good values for all the weights and the biases from Labeled examples. In Supervised Learning, a Machine Learning Algorithm builds a Model by examining many examples and attempting to find a Model that minimizes loss which is called Empirical Risk Minimization. On my Journey of Machine Learning and Deep Learning, Today I have started learning from the **Machine Learning Crash Course** of Google. Here, I have learned about Machine Learning Philosophy, Fundamentals of Machine Learning and Uses, Labels and Features, Labeled and Unlabeled Example, Models and Inference, Regression and Classification, Linear Regression, Weights and Bias, Training and Loss, Empirical Risk Minimization, Mean Squared Error or MSE, Reducing Loss, Gradient Descent and few more Topics related to the same. I have presented the simple Implementation of Basic Recurrent Neural Network here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!\n- Course:\n  - [**Machine Learning Crash Course**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ae75d0887661.png)\n\n**Day46 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have learned from **Machine Learning Crash Course** of Google. Here, I have learned and Implemented about Learning Rate or Step size, Hyperparameters in Machine Learning Algorithms, Regression, Gradient Descent, Optimizing Learning Rate, Stochastic Gradient Descent or SGD, Batch and Batch Size, Minibatch Stochastic Gradient Descent, Convergence, Hierarchy of TensorFlow Toolkits and few more Topics related to the same. I have also spend some time in reading the Book **Speech and Language Processing**. Here, I have read about Regular Expressions and Patterns, Precision and Recall, Kleene Star, Aliases for Common Characters, RE Operators for Counting and few more Topics related to the same. I have presented the simple Implementation of Recurrent Neural Network and Deep RNN using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course and Book mentioned above and below. Excited about the days ahead !!\n- Course:\n  - [**Machine Learning Crash Course**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n- Book:\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8fab746b7f17.png)\n\n**Day47 of 300DaysOfData!**\n- **Feature Vector and Feature Engineering**: Feature Engineering means transforming Raw Data into Feature Vector, which is the set of Floating values comprising the examples of the Dataset. On my Journey of Machine Learning and Deep Learning, Today I have learned from **Machine Learning Crash Course** of Google. Here, I have learned and Implemented about Generalization of Model, Overfitting, Gradient Descent and Loss, Statistical and Computational Learning Theories, Stationarity of Data, Splitting of Data and Validation Set, Representation and Feature Engineering, Feature Vector, Categorical Features and Vocabulary, One Hot Encoding and Sparse Representation, Qualities of Good Features and few more Topics related to the same. I have presented the simple Implementation of RNN along with GRU here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course and Book mentioned above and below. Excited about the days ahead !!\n- Course:\n  - [**Machine Learning Crash Course**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9e8d9bf7fc77.png)\n\n**Day48 of 300DaysOfData!**\n- **Scaling Features**: Scaling means converting floating point Feature Values from their Natural range into Standard range such as 0 to 1. If the Feature set contains multiple Features, then Feature Scaling helps Gradient Descent to converge more quickly. On my Journey of Machine Learning and Deep Learning, Today I have learned from **Machine Learning Crash Course** of Google. Here, I have learned and Implemented about Scaling Feature Values, Handling Extreme Outliers, Binning, Scrubbing the Data, Standard Deviation, Feature Cross and Synthetic Feature, Encoding Nonlinearity, Stochastic Gradient Descent, Cross Product, Crossing One Hot Vectors, Regularization For Simplicity, Generalization Curve, L2 Regularization, Early Stopping, Lambda and Learning Rate and few more Topics related to the same. I have presented the simple Implementation of Linear Regression Model using Sequential API here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!\n- Course:\n  - [**Machine Learning Crash Course**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_381178f54eb1.png)\n\n**Day49 of 300DaysOfData!**\n- **Prediction Bias**: Prediction Bias is a quantity that measures how far apart is the average of predictions from the average of labels in Dataset. Prediction Bias is completely a different quantity than Bias. On my Journey of Machine Learning and Deep Learning, Today I have learned from **Machine Learning Crash Course** of Google. Here, I have learned and Implemented about Logistic Regression and Calculating Probability, Sigmoid Function, Binary Classification, Log Loss and Regularization, Early Stopping, L1 and L2 Regularization, Classification and Thresholding, Confusion Matrix, Class Imbalance and Accuracy, Precision and Recall, ROC Curve, Area Under Curve or AUC, Prediction Bias, Calibration Layer, Bucketing, Sparsity, Feature Cross and One Hot Encoding and few more Topics related to the same. I have presented the simple Implementation of Normalization and Binary Classification using Keras here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!\n- Course:\n  - [**Machine Learning Crash Course**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f10c761ec855.png)\n\n**Day50 of 300DaysOfData!**\n- **Categorical Data and Sparse Tensors**: Categorical Data refers to Input Features that represent one or more Discrete items from a finite set of choices. Sparse Tensors are the tensors with very few non zero elements. On my Journey of Machine Learning and Deep Learning, Today I have learned from **Machine Learning Crash Course** of Google. Here, I have learned and Implemented about Neural Networks, Hidden Layers and Activation Functions, Nonlinear Classification and Feature Crosses, Sigmoid Function, Rectified Linear Unit or ReLU, Backpropagation, Vanishing and Exploding Gradients, Dropout Regularization, Multi Class Neural Networks, Softmax, Logistic Regression, Embeddings, Collaborative Filtering, Sparse Features, Principal Component Analysis, Word2Vec and few more Topics related to the same. I have presented the simple Implementation of Deep Neural Networks in Multi Class Classification here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Course mentioned above and below. Excited about the days ahead !!\n- Course:\n  - [**Machine Learning Crash Course**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cb16cd1a41f9.png)\n\n**Day51 of 300DaysOfData!**\n- **Deep Learning**: Deep Learning is the general class of Algorithms which falls under Artificial Intelligence and deals with training Mathematical entities named Deep Neural Networks by presenting the instructive examples. It uses large amounts of Data to approximate Complex Functions. On my Journey of Machine Learning and Deep Learning, Today I have started reading and Implementing from the Book **Deep Learning with PyTorch**. Here, I have learned about Core PyTorch, Deep Learning Introduction and Revolution, Tensors and Arrays, Deep Learning Competitive Landscape, Utility Libraries, Pretrained Neural Network that recognizes the subject of an Image, ImageNet, Image Recognition, AlexNet and ResNet, Torch Vision Module and few more Topics related to the same from here. I have presented the Implementation of Obtaining Pretrained Neural Networks for Image Recognition using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1d1125f28943.png)\n\n**Day52 of 300DaysOfData!**\n- **The GAN Game**: GAN stands for Generative Adversarial Network where Generative means something being created, Adversarial means the two Neural Networks are competing to out smart the other and well Network means Neural Networks. A Cycle GAN can turn Images of one Domain into Images of another Domain without the need for us to explicitly provide matching pairs in the Training set. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Pretrained Models, Generative Adversarial Network or GAN, ResNet Generator and Discriminator Models, Cycle GAN Architecture, Torch Vision Module, Deep Fakes, A Neural Network that turns Horses into Zebras and few more Topics related to the same from here. I have presented the Implementation of Cycle GAN that turns Horses into Zebras using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7c6fda44cc9a.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_04cdcfd8f5c0.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1be073233530.png)\n\n**Day53 of 300DaysOfData!**\n- **Tensors and Multi Dimensional Arrays**: Tensors are the Fundamental Data Structure in PyTorch. A Tensor is an array that is a Data Structure which stores a collection of numbers that are accessible individually using a index and that can be indexed with multiple indices. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about A Pretrained Neural Network that describes the scenes, NeuralTalk2 Model, Recurrent Neural Network, Torch Hub, Fundamental Building Block: Tensors, The world as Floating Point Numbers, Multidimensional Arrays and Tensors, Lists and Indexing Tensors, Named Tensors, Einsum, Broadcasting and few more Topics related to the same from here. I have presented the simple Implementation of Indexing Tensors and Named Tensors using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_56e8d6db2840.png)\n\n**Day54 of 300DaysOfData!**\n- **Tensors and Multi Dimensional Arrays**: Tensors are the Fundamental Data Structure in PyTorch. A Tensor is an array that is a Data Structure which stores a collection of numbers that are accessible individually using a index and that can be indexed with multiple indices. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Named Tensors, Changing the names of Named Tensors, Broadcasting Tensors, Unnamed Dimensions, Tensor Element Types, Specifying the Numeric Data Type, The Tensor API, Creation Operations, Indexing, Random Sampling, Serialization, Parallelism, Tensors Storage, Referencing Storage, Indexing into Storage and few more Topics related to the same from here. I have presented the simple Implementation of Named Tensors, Tensor Datatype Attributes and Tensor API using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d62469deb00c.png)\n\n**Day55 of 300DaysOfData!**\n- **Encoding Color Channels**: The most common way to encode Colors into numbers is RGB where a color is defined by three numbers representing the Intensity of Red, Green and Blue. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Tensors Metadata such as Size, Offset and Stride, Transposing Tensors without Copying, Transposing in Higher Dimensions, Contiguous Tensors, Managing Tensors Device Attribute such as moving to GPU and CPU, Numpy Interoperability, Generalized Tensors, Serializing Tensors, Data Representation using Tensors, Working with Images, Adding Color Channels, Changing the Layout and few more Topics related to the same from here. I have presented the Implementation of Working with Images such as Changing the Layout and Permute method along with Contiguous Tensors using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3065aecafccd.png)\n\n**Day56 of 300DaysOfData!**\n- **Continuous, Ordinal and Categorical Values**: Continuous Values are the values which can be counted and measured along with units. Ordinal Values are the continuous values with no fixed relationships between values. Categorical Values are the enumerations of possibilities. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Normalizing the Image Data, Working with 3D Images or Volumetric Image Data, Representing the Tabular Data, Loading the Data Tensors using Numpy, Continuous Values, Ordinal Values, Categorical Values, Ratio Scale and Interval Scale, Nominal Scale, One Hot Encoding and Embeddings, Singleton Dimensions and few more Topics related to the same from here. I have presented the Implementation of Normalizing the Image Data, Volumetric Data, Tabular Data and One Hot Encoding using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_45e72e057a9d.png)\n\n**Day57 of 300DaysOfData!**\n- **Continuous, Ordinal and Categorical Values**: Continuous Values are the values which can be counted and measured along with units. Ordinal Values are the continuous values with no fixed relationships between values. Categorical Values are the enumerations of possibilities. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Continuous and Categorical Data, PyTorch Tensor API, Finding Thresholds in Tabular Data, Advanced Indexing, Working with Time Series Data, Adding Time Dimension in Data, Shaping the Data by Time Period, Tensors and Arrays and few more Topics related to the same from here. I have presented the Implementation of Working with Categorical Data, Time Series Data and Finding Thresholds using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0e5e5321714f.png)\n\n**Day58 of 300DaysOfData!**\n- **Encoding and ASCII**: Every written characters is represented by a code which refers to a sequence of bits of appropriate length so that each character can be uniquely identified and it is called Encoding. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Working with Time Series Data, Ordinal Variables, One Hot Encoding and Concatenation, Unsqueeze and Singleton Dimension, Mean, Standard Deviation and Rescaling Variables, Text Representation, Natural Language Processing and Recurrent Neural Networks, Converting the Text into Numbers, Project Gutenberg Corpus, One Hot Encoding of Characters, Encoding and ASCII, Embeddings and Processing the Text and few more Topics related to the same from here. I have presented the Implementation of Time Series Data and Text Representation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ef957bab8bf2.png)\n\n**Day59 of 300DaysOfData!**\n- **Loss Function**: Loss Function is a function that computes a single numerical value that the learning process will attempt to minimize. The calculation of loss typically involves taking the difference between the desired outputs for some training samples. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about One Hot Encoding and Vectors, Data Representation using Tensors, Text Embeddings, Natural Language Processing, The Mechanics of Learning, Johannes Kepler's Lesson in Modeling, Eccentricity, Parameter Estimation, Weight, Bias and Gradients, Simple Linear Model, Loss Function or Cost Function, Mean Square Loss, Broadcasting and few more Topics related to the same from here. I have presented the simple Implementation of Representing Text, Mechanics of Learning and Simple Linear Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2dad74c7a917.png)\n\n**Day60 of 300DaysOfData!**\n- **Gradient Descent**: Gradient Descent is the first order iterative Optimization Algorithm for finding a local minimum of a Differentiable Function. Simply, Gradient is the derivates of the Function with respect to each Parameter. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Cost Function or Loss Function, Optimizing Parameters using Gradient Descent, Decreasing Loss Function, Parameter Estimation, Mechanics of Learning, Scaling Factor and Learning Rate, Evaluations of Model, Computing the Derivative of Loss Function and Linear Function, Defining Gradient Function, Partial Derivative and Iterating the Model, The Training Loop and few more Topics related to the same from here. I have presented the Implementation of Loss Function, Computing Derivatives, Gradient Function and Training Loop here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fbe232c155fe.png)\n\n**Day61 of 300DaysOfData!**\n- **Hyperparameter Tuning**: Hyperparameter Tuning refers to the Training of Model's parameters and hyperparameters control how the Training goes. Hyperparameters are generally set manually. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Gradient Descent, Optimizing the Training Loop, Overtraining, Convergence and Divergence, Learning Rate, Hyperparameter Tuning, Normalizing the Inputs, Visualization or Plotting the Data, Argument Unpacking, PyTorch's Autograd and Backpropagation, Chain Rule, Linear Model and few more Topics related to the same from here. I have presented the simple Implementation of Training Loop and Gradient Descent along with Visualization using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4afcb6129223.png)\n\n**Day62 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Gradient Descent, PyTorch's Autograd and Backpropagation, Chain Rule and Tensors, Grad Attribute and Parameters, Simple Linear Function and Simple Loss Function, Accumulating Grad Functions, Zeroing the Gradients, Autograd Enabled Training Loop, Optimizers and Vanilla Gradient Descent and Optim Submodule of Torch and few more Topics related to the same from here. I have presented the simple Implementation of Linear Model and Loss Function, Autograd Enabled Training Loop using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ac2649d59ca.png)\n\n**Day63 of 300DaysOfData!**\n- **Stochastic Gradient Descent**: Stochastic Gradient Descent or SGD comes from the fact that the Gradient is typically obtained by averaging over a random subset of all Input samples. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Optimizers, Vanilla Gradient Descent Optimization, Stochastic Gradient Descent, Momentum Argument, Minibatch, Learning Rate and Params, Optim Module, Neural Network Models, Adam Optimizers, Backpropagation, Optimizing Weights, Training, Validation and Overfitting, Evaluating the Training Loss, Generalizing to the Validation Set, Overfitting and Penalization Terms and few more Topics related to the same from here. I have presented the Implementation of SGD and Adam Optimizer along with the Training Loop here in the Snapshots. It is the continuation of the previous Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9f4d35e984fb.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5a2e9b354ca4.png)\n\n**Day64 of 300DaysOfData!**\n- **Activation Functions**: Activation Functions are Nonlinear which allows the overall network to approximate more complex functions. They are differentiable so that Gradients can be computed through them. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I am learning to use a Neural Network to fit the Data, Artificial Neurons, The Learning Process and Loss Function, Non Linear Activation Functions, Weights and Biases, Composing a Multilayer Network, Understanding the Error Function, Capping and Compressing the Output Range, Tanh and ReLU Activations, Choosing the Activation Functions, The PyTorch NN Module and few more Topics related to the same from here. I have presented the simple Implementation of Linear Model and Training Loop using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_760518bba361.png)\n\n**Day65 of 300DaysOfData!**\n- **Activation Functions**: Activation Functions are Nonlinear which allows the overall network to approximate more complex functions. They are differentiable so that Gradients can be computed through them. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about The PyTorch NN Module, Simple Linear Model, Batching Input Data, Optimizing Batches, Mean Square Error Loss Function, Training Loop, Neural Networks, Sequential Model, Tanh Activation Function, Inspecting Parameters, Weights and Biases, OrderedDict Module, Comparing to the Linear Model, Overfitting and few more Topics related to the same form here. I have presented the simple Implementation of Sequential Model and OrderedDict Submodule using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a1fc27b89c98.png)\n\n**Day66 of 300DaysOfData!**\n- **Computer Vision**: Computer Vision is an Interdisciplinary scientific field that deals with how computers can gain high level understanding from digital images or videos. It seeks to understand and automate tasks that the human visual system can do. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have started the new Topic Learning From Images. I have learned about Simple Image Recognition, CIFAR10 which is a Dataset of Tiny Images, Torch Vision Module, The Dataset Class, Iterable Dataset, Python Imaging Library or PIL Package, Dataset Transforms, Arrays and Tensors, Permute Function and few more Topics related to the same. I have presented the simple Implementation of Torch Vision Module along with CIFAR10 Dataset using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dbae9ccf0d9c.png)\n\n**Day67 of 300DaysOfData!**\n- **Computer Vision**: Computer Vision is an Interdisciplinary scientific field that deals with how computers can gain high level understanding from digital images or videos. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Permutation Function, Normalizing the Data, Stacking, Mean and Standard Deviation, Torch Vision Module and Submodules, CIFAR10 Dataset, PIL Package, Image Recognition, Building the Dataset, Building a fully connected Neural Networks Model, Sequential Model, Simple Linear Model, Classification and Regression Problems, One Hot Encoding and Softmax and few more Topics related to the same from here. I have presented the Implementation of Normalizing the Data, Building the Dataset and Neural Network Model using Torch Vision Modules here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fbfab3cffe2a.png)\n\n**Day68 of 300DaysOfData!**\n- **Softmax Function**: Softmax Function is a type of function that takes a vector of values and produces another vector of the same dimension where the values satisfy the constraints presented as Probabilities. Softmax is a monotone function that the lower values in the input will correspond to lower values in the output. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Representing Output as Probabilities and Softmax Function, PyTorch's NN Module, Backpropagation, A Loss for Classification, MSE Loss, Negative Log Likelihood or NLL Loss, Log Softmax Function, Training the Classifier, Stochastic Gradient Descent, Hyperparameters, Minibatches and few more Topics related to the same from here. I have presented the Implementation of Softmax Function, Building Neural Network Model and Training Loop using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1c3d91bd70e7.png)\n\n**Day69 of 300DaysOfData!**\n- **Cross Entropy Loss**: Cross Entropy Loss is a negative log likelihood of the predicted distribution under the target distribution as an outcome. The combination of Log Softmax Function and NLL Loss Function is equivalent to using Cross Entropy Loss. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Gradient Descent, Minibatches and Data Loader, Stochastic Gradient Descent, Neural Network Model, Log Softmax Function, NLL Loss Function, Cross Entropy Loss Function, Trainable Parameters, Weights an Biases, Translation Invariant, Data Augment, Torch Vision and NN Modules and few more Topics related to the same from here. I have presented the Implementation of Building Deep Neural Network, Training Loop and Model Evaluation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a92abc4c8120.png)\n\n**Day70 of 300DaysOfData!**\n- **Translational Invariance**: Translational Invariance makes the Convolutional Neural Network invariant to translation which means that if we translate the Inputs then the CNN will still be able to detect the class to which the Input belongs. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have started reading the Topic Using Convolutions to Generalize. I have learned about Convolutional Neural Network, Translation Invariant, Weights and Biases, Discrete Cross Correlations, Locality or Local Operations on Neighborhood Data, Model Parameters, Multi Channel Image, Padding the Boundary, Kernel Size, Detecting Features with Convolutions and few more Topics related to the same. I have presented the simple Implementation of CNN and Building the Data using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0651c18b3c1b.png)\n\n**Day71 of 300DaysOfData!**\n- **Down Sampling**: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Kernel Size, Padding the Image, Edge Detection Kernel, Locality and Translation Invariant, Learning Rate and Weight Update, Max Pooling Layer and Down Sampling, Stride, Convolutional Neural Networks, Receptive Field, Tanh Activation Function, Simple Linear Model, Sequential Model, Parameters of the Model and few more Topics related to the same from here. I have presented the Implementation of Convolutional Neural Network, Plotting the Image and Inspecting the Parameters of the Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1e44aea17000.png)\n\n**Day72 of 300DaysOfData!**\n- **Down Sampling**: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Sub Classing the NN Module, The Sequential or The Modular API, Forward Function, Linear Model, Max Pooling Layer, Padding the Data, Convolutional Neural Network Architecture, ResNet, Kernel Size and Attributes, Tanh Activation Function, Model Parameters, The Functional API, Stateless Modules and few more Topics related to the same from here. I have presented the Implementation of Sub Classing the NN Module using The Sequential API and The Functional API using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c637bdb00a1b.png)\n\n**Day73 of 300DaysOfData!**\n- **Down Sampling**: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about The Torch NN Module, The Functional API, Convolutional Neural Network and The Training, The Data Loader Module, Forward and Backward Pass of the Network, Stochastic Gradient Descent Optimizer, Zeroing the Gradients, Cross Entropy Loss Function, Model Evaluation and Gradient Descent and few more Topics related to the same from here. I have presented the Implementation of Training Loop and Model Evaluation using PyTorch here in the Snapshot. Actually, It is the continuation of yesterday's Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_022f87f2e1e4.png)\n\n**Day74 of 300DaysOfData!**\n- **Down Sampling**: Down Sampling is the scaling of an Image by half which is equivalent of taking four neighboring pixels as input and producing one pixel as Output. Down Sampling principle can be implemented in different ways such as Max Pooling. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Saving and Loading the Model, Weights and Parameters of the Model, Training the Model on GPU, The Torch NN Module and Sub Modules, Map Location Keyword, Designing Model, Long Short Term Memory or LSTM, Adding Memory Capacity or Width to the Network, Feed Forward Network, Overfitting and few more Topics related to the same from here. I have presented the Implementation of Adding Memory Capacity or Width to the Network using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_adc6195bf8a7.png)\n\n**Day75 of 300DaysOfData!**\n- **L2 Regularization**: L2 Regularization is the sum of the squares of all the weights in the Model whereas L1 Regularization is the sum of the absolute values of all the weights in the Model. L2 Regularization is also referred to as Weight Decay. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Convolutional Neural Network, L2 Regularization and L1 Regularization, Optimization and Generalization, Weight Decay, The PyTorch NN Module and Sub Modules, Stochastic Gradient Descent Optimizer, Overfitting and Dropout, Deep Neural Networks, Randomization and few more Topics related to the same from here. I have presented the Implementation of L2 Regularization and Dropout Layer using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e0fad874ecc4.png)\n\n**Day76 of 300DaysOfData!**\n- **L2 Regularization**: L2 Regularization is the sum of the squares of all the weights in the Model whereas L1 Regularization is the sum of the absolute values of all the weights in the Model. L2 Regularization is also referred to as Weight Decay. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Dropout Module, Batch Normalization and Non Linear Activation Functions, Regularization and Principled Augmentation, Convolutional Neural Networks, Minibatch and Standard Deviation, Deep Neural Networks and Depth Module, Skip Connections Mechanism, ReLU Activation Function, Implementation of Functional API and few more Topics related to the same from here. I have presented the Implementation of Batch Normalization and Deep Neural Networks and Depth Module using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f3149ebdfe17.png)\n\n**Day77 of 300DaysOfData!**\n- **Identity Mapping**: When the output of the first activations is used as the input of the last in addition to the standard feed forward path then it is called the Identity Mapping. Identity Mapping alleviate the issues of vanishing gradients. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Convolutional Neural Networks, Skip Connections, ResNet Architecture, Simple Linear Layer, Max Pooling Layer, Identity Mapping, Highway Networks, UNet Model, Dense Networks and Very Deep Neural Networks, Sequential and Functional API, Forward and Backpropagation, Torch Vision Module and Sub Modules, Batch Normalization Layer, Custom Initializations and few more Topics related to the same from here. I have presented the Implementation of ResNet Architecture and Very Deep Neural Networks using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b4b7da562d22.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d545297596c4.png)\n\n**Day78 of 300DaysOfData!**\n- **Voxel**: A Voxel is the 3D equivalent to the familiar 2D pixel. It encloses a volume of space rather than an area. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about CT Scan Dataset, Voxel, Segmentation, Grouping and Classification, Nodules, 3D Convolutions, Neural Networks, Downloading the LUNA Dataset, Data Loading, Parsing the Data, Training and Validation Set and few more Topics related to the same from here. I have started working with LUNA Dataset which stands for Lung Nodule Analysis 2016. The LUNA Grand Challenge is the combination of an open dataset with high quality labels of patient CT scans: many with lung nodules and a public ranking of classifiers against the data. I have presented the Implementation of Preparing the Data using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_39e3719ca4d5.png)\n\n**Day79 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Data Loading and Parsing the Data, CT Scan Dataset, Data Pipeline and few more Topics related to the same from here. Besides, I have also learned about Auto Encoders, Recurrent Neural Networks and Long Short Term Memory or LSTM, Data Processing, One Hot Encoding, Random Splitting of Training and Validation Dataset and few more. I have continued working with LUNA Dataset which stands for Lung Nodule Analysis 2016. The LUNA Grand Challenge is the combination of an open dataset with high quality labels of patient CT scans: many with lung nodules and a public ranking of classifiers against the data. I have presented the simple Implementation of Data Preparation using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c117322a035f.png)\n\n**Day80 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about Loading the Individual CT Scans Dataset, 3D Nodules Density Data, SimpleITK Library, Hounsfield Units, Voxels, Batch Normalization, Loading a Nodule using the Patient Coordinate System, Converting between Millimeters and Voxel Addresses, Array Coordinates, Matrix Multiplication and few more Topics related to the same from here. Besides I have also learned about Auto Encoders using LSTM, Stateful Decoder Model and Data Visualization. I have continued working with LUNA Dataset which stands for Lung Nodule Analysis 2016. I have presented the Implementation of Conversion between Patient Coordinates and Arrays Coordinates on CT Scans Dataset using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_af96d912394f.png)\n\n**Day81 of 300DaysOfData!**\n- **Voxel and Nodules**: A Voxel is the 3D equivalent to the familiar 2D pixel. It encloses a volume of space rather than an area. A mass of tissue made of proliferating cell in the lung is called a Tumor. A small Tumor just a few millimeters wide is called a Nodules. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Deep Learning with PyTorch**. Here, I have learned about PyTorch Dataset Instance Implementation, LUNA Dataset Class, Cross Entropy Loss, Positive and Negative Nodules, Arrays and Tensors, Caching Candidate Arrays, Training and Validation Datasets, Data Visualization and few more Topics related to the same from here. Besides I have also learned about about Normalization of Data, Variance Threshold, RDKIT Library and few more Topics related to the same. I have presented the Implementation of Preparing the LUNA Dataset using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Deep Learning with PyTorch**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8e811f837261.png)\n\n**Day82 of 300DaysOfData!**\n- **Tagging Algorithms**: The problem of learning to predict classes that are not mutually exclusive is called Multilabel Classification. Auto Tagging Problems are best described as Multilabel Classification Problems. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about A Motivating Example on Machine Learning, Learning Algorithms, Training Process, Data, Features, Models, Objective Functions, Optimization Algorithms, Supervised Learning, Regression, Binary, Multiclass and Hierarchical Classification, Cross Entropy and Mean Squared Error Loss Functions, Gradient Descent, Tagging Algorithms and few more Topics related to the same from here. I have presented the Implementation of Preparing the Data, Normalization, Removing Low Variance Features and Data Loaders using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98a3ce066966.png)\n\n**Day83 of 300DaysOfData!**\n- **Reinforcement Learning**: Reinforcement Learning gives a very general statement of problem in which an agent interacts with the environment over a series of time steps and receives some observation and must choose action. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Search Algorithms, Recommender Systems, Sequence Learning, Tagging and Parsing, Machine Translation, Unsupervised Learning, Interacting with an Environment and Reinforcement Learning, Data Manipulation, Mathematical Operations, Broadcasting Mechanisms, Indexing and Slicing, Saving Memory in Tensors, Conversion to Other Datatypes and few more Topics related to the same from here. I have presented the Implementation of Mathematical Operations, Tensors Concatenation, Broadcasting Mechanisms and Datatypes Conversion using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fd6f21a32f20.png)\n\n**Day84 of 300DaysOfData!**\n- **Tensors**: Tensors refer to algebraic objects describing the n dimensional arrays with an arbitrary number of axes. Vectors are first order Tensors and Matrices are second order Tensors. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Data Processing, Reading the Dataset, Handling the Missing Data, Categorical Data, Conversion to the Tensor Format, Linear Algebra such as Scalars, Vectors, Length, Dimensionality and Shape, Matrices, Symmetric Matrix, Tensors, Basic Properties of Tensor Arithmetic, Reduction, Non Reduction Sum, Dot Products, Matrix Vector Products and few more Topics related to the same from here. I have presented the Implementation of Data Processing, Handling the Missing Data, Scalars, Vectors, Matrices and Dot Products using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f0f412a5b340.png)\n\n**Day85 of 300DaysOfData!**\n- **Method of Exhaustion**: The ancient process of finding the area of curved shapes such as circle by inscribing the polygons in such shapes which better approximate the circle is called the Method of Exhaustion. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Matrix Multiplication, L1 and L2 Normalization, Frobenius Normalization, Calculus, Method of Exhaustion, Derivatives and Differentiation, Partial Derivatives, Gradient Descents, Chain Rule, Automatic Differentiation, Backward for Non Scalar Variables, Detaching Computation, Backpropagation, Computing the Gradient with Control Flow and few more Topics related to the same from here. I have presented the Implementation of Matrix Multiplication, L1, L2 and Frobenius Normalization, Derivatives and Differentiation, Automatic Differentiation and Computing the Gradient using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5b1d1d6e382e.png)\n\n**Day86 of 300DaysOfData!**\n- **Method of Exhaustion**: The ancient process of finding the area of curved shapes such as circle by inscribing the polygons in such shapes which better approximate the circle is called the Method of Exhaustion. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Probabilities, Basic Probability Theory, Sampling, Multinomial Distribution, Axioms of Probability Theory, Random Variables, Dealing with Multiple Random Variables, Joint Probability, Conditional Probability, Bayes Theorem, Marginalization, Independence and Dependence, Expectation and Variance, Finding Classes and Functions in a Module and few more Topics related to the same from here. I have presented the Implementation of Multinomial Distribution, Visualization of Probabilities, Derivatives and Differentiation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5a7859144653.png)\n\n**Day87 of 300DaysOfData!**\n- **Hyperparameters**: The parameters that are tunable but not updated in the training loop are called Hyperparameters. Hyperparameters Tuning is the process by which hyperparameters are chosen and typically requires adjusting based on the results of the Training Loop. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Linear Regression, Basic Elements of Linear Regression, Linear Model and Transformation, Loss Function, Analytic Solution, Minibatch Stochastic Gradient Descent, Making Predictions with the Learned Model, Vectorization of Speed, The Normal Distribution and Squared Loss, Linear Regression to Deep Neural Networks, Biological Interpretation, Hyperparameters Tuning and few more Topics related to the same from here. I have presented the Implementation of Vectorization of Speed and Normal Distributions using Python here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ef290516d277.png)\n\n**Day88 of 300DaysOfData!**\n- **Hyperparameters**: The parameters that are tunable but not updated in the training loop are called Hyperparameters. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Linear Regression Implementation From Scratch, Data Pipeline, Deep Learning Frameworks, Generating the Artificial Dataset, Scatter Plot and Correlation, Reading the Dataset, Minibatches, Features and Labels, Parallel Computing, Initializing the Model Parameters, Minibatch Stochastic Gradient Descent, Defining the Simple Linear Regression Model, Broadcasting Mechanism, Vectors and Scalars and few more Topics related to the same from here. I have presented the Implementation of Generating the Synthetic Dataset, Generating the Scatter Plot, Reading the Dataset, Initializing the Model Parameters and Defining the Linear Regression Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a989772c00da.png)\n\n**Day89 of 300DaysOfData!**\n- **Linear Regression**: Linear Regression is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables also known as dependent variables and independent variables. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Linear Regression, Defining the Loss Function, Defining the Optimization Algorithm, Minibatch Stochastic Gradient Descent, Training the Model, Tensors and Differentiation, Concise Implementation of Linear Regression, Generating the Synthetic Dataset, Model Evaluation and few more Topics related to the same from here. I have presented the Implementation of Defining the Loss Function, Minibatch Stochastic Gradient Descent, Training and Evaluating the Model, Concise Implementation of Linear Regression and Reading the Dataset using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3b62e29647d8.png)\n\n**Day90 of 300DaysOfData!**\n- **Linear Regression**: Linear Regression is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables also known as dependent variables and independent variables. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Softmax Regression, Classification Problem, Network Architecture, Parameterization Cost of Fully Connected Layers, Softmax Operation, Vectorization for Minibatches, Loss Function, Log Likelihood, Softmax and Derivatives, Cross Entropy Loss, Information Theory Basics, Entropy and Surprisal, Model Prediction and Evaluation, The Image Classification Dataset and few more Topics related to the same from here. I have presented the Implementation of Image Classification Dataset, Visualization, Softmax Regression and Operation along with Model Parameters using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3af372a9cf72.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_37ee46639977.png)\n\n**Day91 of 300DaysOfData!**\n- **Activation Functions**: Activation Functions decide whether a neuron should be activated or not by calculating the weighted sum and further adding bias with it. They are differentiable operators. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Cross Entropy Loss Function, Classification Accuracy and Training, Softmax Regression, Model Parameters, Optimization Algorithms, Multi Layer Perceptrons, Hidden Layers, Linear Models Problems, From Linear to Nonlinear Models, Universal Approximators, Activation Functions like RELU Function, Sigmoid Function, Tanh Function, Derivatives and Gradients and few more Topics related to the same from here. I have presented the Implementation of Softmax Regression Model, Classification Accuracy, RELU Function, Sigmoid Function, Tanh Function along with Visualizations using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7b724143f6d1.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f93b1679177f.png)\n\n**Day92 of 300DaysOfData!**\n- **Activation Functions**: Activation Functions decide whether a neuron should be activated or not by calculating the weighted sum and further adding bias with it. They are differentiable operators. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Implementation of Multi Layer Perceptrons, Initializing Model Parameters, RELU Activation Functions, Cross Entropy Loss Function, Training the Model, Fully Connected Layers, Simple Linear Layer, Softmax Regression and Function, Stochastic Gradient Descent, Sequential API, High Level APIs, Learning Rate, Weights and Biases, Tensors, Hyperparameters and few more Topics related to the same from here. I have presented the Implementation of Multi Layer Perceptrons, RELU Activation Function, Training the Model and Model Evaluations using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f210638c105a.png)\n\n**Day93 of 300DaysOfData!**\n- **Multi Layer Perceptrons**: The simplest deep neural networks are called Multi Layer Perceptrons. They consist of multiple layers of Neurons. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Model Selection, Underfitting, Overfitting, Training Error and Generalization Error, Statistical Learning Theory, Model Complexity, Early Stopping, Training, Testing and Validation Dataset, K-Fold Cross Validation, Dataset Size, Polynomial Regression, Generating the Dataset, Training and Testing the Model, Third Order Polynomial Function Fitting, Linear Function Fitting, High Order Polynomial Function Fitting, Weight Decay, Normalization and few more Topics related to the same from here. I have presented the Implementation of Generating the Dataset, Defining the Training Function and Polynomial Function Fitting using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3658210dc39e.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e4c6e525ddf8.png)\n\n**Day94 of 300DaysOfData!**\n- **Multi Layer Perceptrons**: The simplest deep neural networks are called Multi Layer Perceptrons. They consist of multiple layers of neurons each fully connected to those in layers below from which they receive input and above which in turn influence. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about High Dimensional Linear Regression, Model Parameters, Defining L2 Normalization Penalty, Defining the Training Loop, Regularization and Weight Decay, Dropout and Overfitting, Bias and Variance Tradeoff, Gaussian Distributions, Stochastic Gradient Descent, Training Error and Test Error and few more Topics related to the same from here. I have presented the Implementation of High Dimensional Linear Regression, Model Parameters, L2 Normalization Penalty, Regularization and Weight Decay using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d81addcc47f5.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dc6be0fd1e76.png)\n\n**Day95 of 300DaysOfData!**\n- **Dropout and Co-adaption**: Dropout is the process of injecting noise while computing each internal layer during forward propagation. Co-adaption is the condition in neural network which is characterized by a state in which each layer relies on the specific pattern of the activations in the previous layer. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Dropout, Overfitting, Generalization Error, Bias and Variance Tradeoff, Robustness through Perturbations, L2 Regularization and Weight Decay, Co-adaption, Dropout Probability, Dropout Layer, Fashion MNIST Dataset, Activation Functions, Stochastic Gradient Descent, The Sequential and Functional API and few more Topics related to the same from here. I have presented the Implementation of Dropout Layer, Training and Testing the Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0904cdd9aae3.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_888865870138.png)\n\n**Day96 of 300DaysOfData!**\n- **Dropout and Co-adaption**: Dropout is the process of injecting noise while computing each internal layer during forward propagation. Co-adaption is the condition in neural network which is characterized by a state in which each layer relies on the specific pattern of the activations in the previous layer. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Forward Propagation, Backward Propagation and Computational Graphs, Numerical Stability, Vanishing and Exploding Gradients, Breaking the Symmetry, Parameter Initialization, Environment and Distribution Shift, Covariate Shift, Label Shift, Concept Shift, Non stationary Distributions, Empirical Risk and True Risk, Batch Learning, Online Learning, Reinforcement Learning and few more Topics related to the same from here. I have presented the Implementation of Data Preprocessing and Data Preparation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n- [**Predicting Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices\u002Fblob\u002Fmain\u002FPredictingHousePrices.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2309b2ccd781.png)\n\n**Day97 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Training and Building Deep Networks, Downloading and Caching Datasets, Data Preprocessing, Regression Problems, Accessing and Reading the Dataset, Numerical and Discrete Categorical Features, Optimization and Variance, Arrays and Tensors, Simple Linear Model, The Sequential API, Root Mean Squared Error, Adam Optimizer, Hyperparameter Tuning, K-Fold Cross Validation, Training and Validation Error, Model Selection, Overfitting and Regularization and few more Topics related to the same from here. I have presented the Implementation of Simple Linear Model, Root Mean Squared Error, Training Function and K-Fold Cross Validation using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n- [**Predicting Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices\u002Fblob\u002Fmain\u002FPredictingHousePrices.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_14e30ac0f165.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_29d5568a2f04.png)\n\n**Day98 of 300DaysOfData!**\n- **Constant Parameters**: Constant Parameters are the terms that are neither the result of the previous layers nor updatable parameters in the Neural Networks. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about K-Fold Cross Validation, Training and Predictions, Hyperparameters Optimization, Deep Learning Computation, Layers and Blocks, Softmax Regression, Multi Layer Perceptrons, ResNet Architecture, Forward and Backward Propagation Function, RELU Activation Function, The Sequential Block Implementation, MLP Implementation, Constant Parameters and few more Topics related to the same from here. I have presented the Implementation of MLP, The Sequential API Class and Forward Propagation Function using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n- [**Predicting Housing Prices**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices\u002Fblob\u002Fmain\u002FPredictingHousePrices.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_200f24416b75.png)\n\n**Day99 of 300DaysOfData!**\n- **Constant Parameters**: Constant Parameters are the terms that are neither the result of the previous layers nor updatable parameters in the Neural Networks. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Parameter Management, Parameter Access, Targeted Parameters, Collecting Parameters from Nested Block, Parameter Initialization, Custom Initialization, Tied Parameters, Deferred Initialization, Multi Layer Perceptrons, Input Dimensions, Defining Custom Layers, Layers without Parameters, Forward Propagation Function, Constant Parameters, Xavier Initializer, Weight and Bias and few more Topics related to the same from here. I have presented the Implementation of Parameter Access, Parameter Initialization, Tied Parameters and Layers without Parameters using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dd29199545c7.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e7f2ef31f21c.png)\n\n**Day100 of 300DaysOfData!**\n- **Invariance and Locality Principle**: Translation Invariance principle states that out network should respond similarly to the same patch regardless of where it appears in the image. Locality Principle states that the network should focus on local regions without regard to the contents of the image in distant regions. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Fully Connected Layers to Convolutions, Translation Invariance, Locality Principle, Constraining the MLP, Convolutional Neural Networks, Cross Correlation, Images and Channels, File IO, Loading and Saving Tensors, Loading and Saving Model Parameters, Custom Layers, Layers with Parameters and few more Topics related to the same from here. I have presented the Implementation of Layers with Parameters, Loading and Saving the Tensors and Model Parameters using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_86e3ac6cef51.png)\n\n**Day101 of 300DaysOfData!**\n- **Invariance and Locality Principle**: Translation Invariance principle states that out network should respond similarly to the same patch regardless of where it appears in the image. Locality Principle states that the network should focus on local regions without regard to the contents of the image in distant regions. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Convolutional Neural Networks, Convolutions for Images, The Cross Correlation Operation, Convolutional Layers, Constructor and Forward Propagation Function, Weight and Bias, Object Edge Detection in Images, Learning a Kernel, Back Propagation, Feature Map and Receptive Field, Kernel Parameters and few more Topics related to the same from here. I have presented the Implementation of Cross Correlation Operation, Convolutional Layers and Learning a Kernel using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9bb441ec8b94.png)\n\n**Day102 of 300DaysOfData!**\n- **Maximum Pooling**: Pooling Operators consist of a fixed shape window that is slid over all the regions in the input according to its stride computing a single output for each location which is either maximum or the average value of the elements in the pooling window. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Padding and Stride, Strided Convolutions, Cross Correlations, Multiple Input and Multiple Output Channels, Convolutional Layer, Maximum Pooling Layer and Average Pooling Layer, Pooling Window and Operators, Convolutional Neural Networks, LeNet Architecture, Supervised Learning, Convolutional Encoder, Sigmoid Activation Function and few more Topics related to the same from here. I have presented the Implementation of CNN, Implementation of Padding, Stride and Pooling Layers, Multiple Channels using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d21a083ab81f.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_06e66bfd0ee4.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f054e0a19483.png)\n\n**Day103 of 300DaysOfData!**\n- **VGG Networks**: VGG Networks construct a network using reusable convolutional blocks. VGG Models are defined by the number of convolutional layers and output channels in each block. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Convolutional Neural Networks, Supervised Learning, Deep CNN and AlexNet, Support Vector Machine and Features, Learning Representations, Data and Hardware Accelerator Problems, Architectures of LeNet and AlexNet, Activation Functions such as ReLU, Networks using CNN Blocks, VGG Neural Networks Architecture, Padding and Pooling, Convolutional Layers, Dropout, Dense and Linear Layers and few more Topics related to the same from here. I have presented the Implementation of AlexNet Architecture and VGG Networks Architecture along with CNN Blocks using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3cce212c56e6.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_50dc1b56d5cb.png)\n\n**Day104 of 300DaysOfData!**\n- **VGG Networks**: VGG Networks construct a network using reusable convolutional blocks. VGG Models are defined by the number of convolutional layers and output channels in each block. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Network In Network or NIN Architecture, NIN Blocks and Model, Convolutional Layer, RELU Activation Function, The Sequential and Functional API, Global Average Pooling Layer, Networks with Parallel Concatenations or GoogLeNet, Inception Blocks, GoogLeNet Model and Architecture, Maximum Pooling Layer, Training the Model and few more Topics related to the same from here. I have presented the Implementation of NIN Block and Model, Inception Block and GoogLeNet Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_250eba7117f5.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_16df9904ab51.png)\n\n**Day105 of 300DaysOfData!**\n- **Batch Normalization**: Batch Normalization continuously adjusts the intermediate output of the neural network by utilizing the mean and standard deviation of the minibatch so that the values of the intermediate output are more stable. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Batch Normalization, Training Deep Neural Networks, Scale Parameter and Shift Parameter, Batch Normalization Layers, Fully Connected Layers, Convolutional Layers, Batch Normalization during Prediction, Tensors, Mean and Variance, Applying BN in LeNet, Concise Implementation of BN using high level API, Internal Covariate Shift, Dropout Layer, Residual Networks or ResNet, Function Classes, Residual Blocks and few more Topics related to the same from here. I have presented the Implementation of Batch Normalization Architecture using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_788cdd325401.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e2298dbf9123.png)\n\n**Day106 of 300DaysOfData!**\n- **Batch Normalization**: Batch Normalization continuously adjusts the intermediate output of the neural network by utilizing the mean and standard deviation of the minibatch so that the values of the intermediate output are more stable. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Densely Connected Neural Networks or DenseNet, Dense Blocks, Batch Normalization, Activation Functions and Convolutional Layer, Transition Layer, Residual Networks or ResNet, Function Classes, Residual Blocks, Residual Mapping, Residual Connection, ResNet Model, Maximum and Average Pooling Layers, Training the Model and few more Topics related to the same from here. I have presented the Implementation of ResNet Architecture and ResNet Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f11ccc27237f.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_42987ac07b27.png)\n\n**Day107 of 300DaysOfData!**\n- **Sequence Models**: The prediction beyond the known observations is called Extrapolation. The estimating between the existing observations is called Interpolation. Sequence Models require specialized statistical tools for estimation such as Auto Regressive Models. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about DenseNet Model, Convolutional Layers, Recurrent Neural Networks, Sequence Models, Interpolation and Extrapolation, Statistical Tools, Autoregressive Models, Latent Autoregressive Models, Markov Models, Reinforcement Learning Algorithms, Causality, Conditional Probability Distribution, Training the MLP, One step ahead prediction and few more Topics related to the same from here. I have presented the Implementation of DenseNet Architectures and Simple Implementation of RNNs using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bfd8a19caee5.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eb08fc134112.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_618be2eee6a8.png)\n\n**Day108 of 300DaysOfData!**\n- **Tokenization and Vocabulary**: Tokenization is the splitting of a string or text into a list of tokens. Vocabulary is the dictionary that maps string tokens into numerical indices. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **ve into Deep Learning**Here, I have learned about Text Preprocessing, Corpus of Text, Tokenization Function, Sequence Models and Dataset, Vocabulary, Dictionary, Multilayer Perceptron, One step ahead prediction, Multi step ahead prediction, Tensors, Recurrent Neural Networks and few more Topics related to the same from here. I have presented the Implementation of Reading the Dataset, Tokenization and Vocabulary using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_81feff0ca6c7.png)\n\n**Day109 of 300DaysOfData!**\n- **Sequential Partitioning**: Sequential Partitioning is the strategy that preserves the order of split subsequences when iterating over minibatches. It ensures that the subsequences from two adjacent minibatches during iteration are adjacent in the original sequence. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Language Models and Sequence Dataset, Conditional Probability, Laplace Smoothing, Markov Models and NGrams, Unigram, Bigram and Trigram Models, Natural Language Statistics, Stop words, Word Frequencies, Zipf's Law, Reading Long Sequence Data, Minibatches, Random Sampling, Sequential Partitioning and few more Topics related to the same from here. I have presented the Implementation of Unigram, Bigram and Trigram Model Frequencies, Random Sampling and Sequential Partitioning using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2894820ea223.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_81b2ba3e6297.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_03cc7cf9636d.png)\n\n**Day110 of 300DaysOfData!**\n- **Recurrent Neural Networks**: Recurrent Neural Networks are the networks that uses recurrent computation for hidden states. The hidden state of an RNN can capture historical information of the sequence up to the current time step. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Recurrent Neural Networks or RNN, Hidden State, Neural Networks without Hidden States, RNNs with Hidden States, RNN Layers, RNN based Character Level Language Models, Perplexity, Implementation of RNN from Scratch, One Hot Encoding, Vocabulary, Initializing the Model Parameters, RNN Model, Minibatch and Tanh Activation Function, Prediction and Warm up period, Gradient Clipping, Backpropagation and few more Topics related to the same from here. I have presented the Implementation RNN Model, Gradient Clipping and Training the Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cd54a74d878e.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5b69b1b64e0b.png)\n\n**Day111 of 300DaysOfData!**\n- **Recurrent Neural Networks**: Recurrent Neural Networks are the networks that uses recurrent computation for hidden states. The hidden state of an RNN can capture historical information of the sequence up to the current time step. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Implementation of Recurrent Neural Networks, Defining the RNN Model, Training and Prediction, Backpropagation through Time, Exploding Gradients, Vanishing Gradients, Analysis of Gradients in RNNs, Full Computation, Truncating Time Steps, Randomized Truncation, Gradient Computing strategies in RNNs, Activation Functions, Regular Truncation and few more Topics related to the same from here. I have presented the Implementation of Recurrent Neural Networks, Training and Prediction using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e065fcfce3eb.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5ad0e694efb3.png)\n\n**Day112 of 300DaysOfData!**\n- **Gated Recurrent Units**: Gated Recurrent Units or GRUs are a gating mechanisms in Recurrent Neural Networks in which hidden state should be updated and also when it should be reset. It aims to solve the vanishing gradient problem which comes with standard RNNs. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Modern Recurrent Neural Networks, Gradient Clipping, Gated Recurrent Units or GRUs, Memory cell, Gated Hidden State, Reset Gate and Update Gate, Broadcasting, Candidate Hidden State, Hadamard Product Operator, Hidden State, Initializing Model Parameters, Defining the GRU Model, Training and Prediction and few more Topics related to the same from here. I have presented the Implementation of Gated Recurrent Units, GRU Model, Training and Prediction using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_92484e8c5773.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fa1a700bbed0.png)\n\n**Day113 of 300DaysOfData!**\n- **Long Short Term Memory**: Long Short Term Memory or LSTM is a type of Recurrent Neural Networks capable of learning order dependence in sequence prediction problems. LSTM has Input Gates, Forget Gates and Output Gates that control the flow of information. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Long Short Term Memory or LSTM, Gated Memory Cell, Input Gate, Forget Gate and Output Gate, Candidate Memory Cell, Tanh Activation Function, Sigmoid Activation Function, Memory Cell, Hidden State, Initializing Model Parameters, Defining the LSTM Model, Training and Prediction, Gated Recurrent Units or GRUs, Gaussian Distribution and few more Topics related to the same from here. I have presented the Implementation of Long Short Term Memory or LSTM Model, Training and Prediction using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1cb795c7d392.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_753817bcb99c.png)\n\n**Day114 of 300DaysOfData!**\n- **Long Short Term Memory**: Long Short Term Memory or LSTM is a type of Recurrent Neural Networks capable of learning order dependence in sequence prediction problems. LSTM has Input Gates, Forget Gates and Output Gates that control the flow of information. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Deep Recurrent Neural Networks, Functional Dependencies, Bidirectional Recurrent Neural Networks, Dynamic Programming in Hidden Markov Models, Bidirectional Model, Computational Cost and Applications, Machine Translation and Dataset, Preprocessing the Dataset, Tokenization, Vocabulary, Padding Text Sequences and few more Topics related to the same from here. I have presented the Implementations of Downloading the Dataset, Preprocessing, Tokenization and Vocabulary using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8b6b7c67afc9.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2549138c4c81.png)\n\n**Day115 of 300DaysOfData!**\n- **Encoder and Decoder Architecture**: Encoder takes a variable length sequence as the input and transforms it into a state with a fixed shape. Decoder maps the encoded state of a fixed shape to a variable length sequence. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Encoder and Decoder Architectures, Machine Translation Model, Sequence Transduction Models, Forward Propagation Function, Sequence to Sequence Learning, Recurrent Neural Networks, Embedding Layer, Gated Recurrent Units or GRU Layers, Hidden States and Units, RNN Encoder and Decoder Architecture, Vocabulary and few more Topics related to the same from here.I have presented the Implementation of Encoder, Decoder Architectures and RNN Encoder Decoder for Sequence to Sequence Learning using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_95be6b759068.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_585f04323f11.png)\n\n**Day116 of 300DaysOfData!**\n- **Sequence Search**: Greedy Search is the conditional probability of generating an output sequence based on the input sequence. Beam Search is an improved version of Greedy Search with a hyperparameter named beam size. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Softmax Cross Entropy Loss Function, Sequence Masking, Teacher Forcing, Training and Prediction, Evaluation of Predicted Sequences, BLEU or Bilingual Evaluation Understudy, RNN Encoder Decoder, Beam Search, Greedy Search, Exhaustive Search, Attention Mechanisms, Attention Cues, Nonvolitional Cue and Volitional Cue, Queries, Keys and Values, Attention Pooling and few more Topics related to the same from here. I have presented the Implementation of Sequence Masking, Softmax Cross Entropy Loss, Training RNN Encoder Decoder Model and BLEU using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_24d936866d99.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f433bd706d3d.png)\n\n**Day117 of 300DaysOfData!**\n- **Attention Pooling**: Attention Pooling selectively aggregates values or sensory inputs to produce the output. It implies the interaction between queries and keys. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Attention Pooling or Nadaraya Watson Kernel Regression, Queries or Volitional Cues and Keys or Non Volitional Cues, Generating the Dataset, Average Pooling, Non Parametric Attention Pooling, Attention Weight, Gaussian Kernel, Parametric Attention Pooling, Batch Matrix Multiplication, Defining the Model, Training the Model, Stochastic Gradient Descent, MSE Loss Function and few more Topics related to the same from here. I have presented the Implementation of Attention Mechanisms, Non Parametric Attention Pooling, Batch Matrix Multiplication, NW Kernel Regression Model, Training and Prediction using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e67983c73ff8.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98615d2603b7.png)\n\n**Day118 of 300DaysOfData!**\n- **Attention Pooling**: Attention Pooling selectively aggregates values or sensory inputs to produce the output. It implies the interaction between queries or volitional cues and keys or non volitional cues. Attention Pooling is the weighted average of the training outputs. It can be parametric or nonparametric. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Attention Scoring Functions, Gaussian Kernel, Attention Weights, Softmax Activation Function, Masked Softmax Operation, Text Sequences, Probability Distribution, Additive Attention, Queries, Keys and Values, Tanh Activation Function, Dropout and Linear Layer, Attention Pooling and few more Topics related to the same from here. I have presented the Implementation of Masked Softmax Operation and Additive Attention using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c7e03d9859e2.png)\n\n**Day119 of 300DaysOfData!**\n- **Attention Pooling**: Attention Pooling selectively aggregates values or sensory inputs to produce the output. It implies the interaction between queries or volitional cues and keys or non volitional cues. Attention Pooling is the weighted average of the training outputs. It can be parametric or nonparametric. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Scaled Dot Product Attention, Queries, Keys and Values, Additive Attention, Attention Pooling, Bahdanau Attention, RNN Encoder Decoder Architecture, Hidden States, Embedding, Defining Decoder with Attention, Sequence to Sequence Attention Decoder and few more Topics related to the same from here.I have presented the Implementation of Scaled Dot Product Attention and Sequence to Sequence Attention Decoder Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5132a646704c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d682cc6b9db7.png)\n\n**Day120 of 300DaysOfData!**\n- **Multi Head Attention**: Multi Head Attention is the design for attention mechanisms which runs through an attention mechanism several times in parallel. Instead of performing single attention pooling, queries, keys and values can be transformed into learned linear projections which are fed into attention pooling in parallel. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Bahdanau Attention, Recurrent Neural Networks Encoder Decoder Architecture, Training the Sequence to Sequence Model, Embedding Layer, Attention Weights, GRU, Heatmaps, Multi Head Attention, Queries, Keys and Values, Attention Pooling, Additive Attention and Scaled Dot Product Attention, Transpose Functions and few more Topics related to the same from here.  I have presented the Implementation Multi Head Attention using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ed65da7ab752.png)\n\n**Day121 of 300DaysOfData!**\n- **Multi Head Attention**: Multi Head Attention is the design for attention mechanisms which runs through an attention mechanism several times in parallel. Instead of performing single attention pooling, queries, keys and values can be transformed into learned linear projections which are fed into attention pooling in parallel. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Multi Head Attention, Queries, Keys and Values, Attention Pooling, Scaled Dot Product Attention, Self Attention and Positional Encoding, Recurrent Neural Networks, Intra Attention, Comparing CNNs, RNNs and Self Attention, Padding Tokens, Absolute Positional Information, Relative Positional Information and few more Topics related to the same from here. I have presented the Implementation of Positional Encoding using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_52c39b78635c.png)\n\n**Day122 of 300DaysOfData!**\n- **Transformer Architecture**: Transformer is an architecture for transforming one sequence into another one with the help of two parts, Encoder and Decoder. It makes the use of Self Attention mechanisms. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Transformer, Self Attention, Encoder and Decoder Architecture, Sequence Embeddings, Positional Encoding, Position Wise Feed Forward Networks, Residual Connection and Layer Normalization, Encoder Block and Multi Head Self Attention, Transformer Decoder, Queries, Keys and Values, Scaled Dot Product Attention and few more Topics related to the same from here. I have presented the Implementation of Position Wise Feed Forward Networks, Residual Connection and Layer Normalization, Encoder, Decoder Block and Transformer Decoder using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_750baaf29402.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_95af79810722.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b7f653ab6266.png)\n\n**Day123 of 300DaysOfData!**\n- **Transformer Architecture**: Transformer is an architecture for transforming one sequence into another one with the help of two parts, Encoder and Decoder. It makes the use of Self Attention mechanisms. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Decoder Architecture, Self Attention, Encoder Decoder Attention, Position Wise Feed Forward Networks, Residual Connections, Transformer Decoder, Embedding Layer, Sequential Blocks, Training the Transformer Architecture and few more Topics related to the same from here. I have also read about Logistic Regression, Sigmoid Activation Function, Weights Initialization, Gradient Descent, Cost Function and more. I have presented the Implementation of Logistic Regression from Scratch using NumPy, Transformer Decoder and Training using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Logistic Regression Docs**](https:\u002F\u002Fml-cheatsheet.readthedocs.io\u002Fen\u002Flatest\u002Flogistic_regression.html)\n  - [**Implementation of Logistic Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Ftree\u002Fmain\u002FLogisticRegression)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_30d0eb86e30c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eff4d2cccee8.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_963bbbeb8b7d.png)\n\n**Day124 of 300DaysOfData!**\n- **Transformer Architecture**: Transformer is an architecture for transforming one sequence into another one with the help of two parts, Encoder and Decoder. It makes the use of Self Attention mechanisms. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Optimization Algorithms and Deep Learning, Objective Function and Minimization, Goal of Optimization, Generalization Error, Training Error, Risk Function and Empirical Risk Function, Optimization Challenges, Local Minimum and Global Minimum, Saddle Points, Hessian Matrix and Eigenvalues, Vanishing Gradients, Convexity, Convex Sets and Functions, Jensen's Inequality and few more Topics related to the same from here. I have presented the Implementation of Local Minima, Saddle Points, Vanishing Gradients and Convex Functions using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_07e8730fcf37.png)\n\n**Day125 of 300DaysOfData!**\n- **Gradient Descent**: Gradient Descent is an optimization algorithm which is used to minimize the differentiable function by iteratively moving in the direction of steepest descent as defined by the negative of the Gradient. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Convexity and Second Derivatives, Constrained Optimization, Lagrangian Function and Multipliers, Penalties, Projections, Gradient Clipping, Stochastic Gradient Descent, One Dimensional Gradient Descent, Objective Function, Learning Rate, Local Minimum and Global Minimum, Multivariate Gradient Descent and few more Topics related to the same from here. I have presented the Implementation of One Dimensional Gradient Descent, Local Minima and Multivariate Gradient Descent using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4b88587d5b0d.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_44cecff1cc78.png)\n\n**Day126 of 300DaysOfData!**\n- **Gradient Descent**: Gradient Descent is an optimization algorithm which is used to minimize the differentiable function by iteratively moving in the direction of steepest descent as defined by the negative of the Gradient. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Multivariate Gradient Descent, Adaptive Methods, Learning Rate, Newtons Method, Taylor Expansion, Hessian Function, Gradient and Backpropagation, Nonconvex Function, Convergence Analysis, Linear Convergence, Preconditioning, Gradient Descent with Line Search, Stochastic Gradient Descent, Loss Functions and few more topics related to the same from here. I have presented the Implementation of Newtons Method, Non Convex Functions and Stochastic Gradient Descent using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0fe15c6f0f8b.png)\n\n**Day127 of 300DaysOfData!**\n- **Stochastic Gradient Descent**: Stochastic Gradient Descent is an iterative method for optimizing an objective function with suitable differentiable properties. It is a variation of the gradient descent algorithm that calculates the error and updates the model. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Stochastic Gradient Descent, Dynamic Learning Rate, Exponential Decay and Polynomial Decay, Convergence Analysis for Convex Objectives, Stochastic Gradient and Finite Samples, Minibatch Stochastic Gradient Descent, Vectorization and Caches, Matrix Multiplications, Minibatches, Variance, Implementation of Gradients and few more topics related to the same from here. I have presented the implementation of Stochastic Gradient Descent and Minibatch Stochastic Gradient Descent using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5226ba7bd254.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6c532f246d65.png)\n\n**Day128 of 300DaysOfData!**\n- **Stochastic Gradient Descent**: Stochastic Gradient Descent is an iterative method for optimizing an objective function with suitable differentiable properties. It is a variation of the gradient descent algorithm that calculates the error and updates the model. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about The Momentum Method, Stochastic Gradient Descent, Leaky Averages, Variance, Accelerated Gradient, An Ill Conditioned Problem and Convergence, Effective Sample Weight, Practical Experiments, Implementation of Momentum with SGD, Theoretical Analysis, Quadratic Convex Functions, Scalar Functions and few more topics related to the same from here. I have presented the implementation of Momentum Method, Effective Sample Weight and Scalar Functions using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead!!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2b73356e7225.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fda22f3cfd2c.png)\n\n**Day129 of 300DaysOfData!**\n- **Stochastic Gradient Descent**: Stochastic Gradient Descent is an iterative method for optimizing an objective function with suitable differentiable properties. It is a variation of the gradient descent algorithm that calculates the error and updates the model. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Adagrad Optimization Algorithms, Sparse Features and Learning Rates, Preconditioning, Stochastic Gradient Descent Algorithm, The Algorithms, Implementation of Adagrad from Scratch, Deep Learning and Computational Constraints, Learning Rates and few more Topics related to the same from here. I have presented the implementation Adagrad Optimization Algorithm from Scratch using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead!!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c0fb6dcb9366.png)\n\n**Day130 of 300DaysOfData!**\n- **RMSProp Optimization Algorithm**: RMSProp is a gradient based optimization algorithm that utilizes the magnitude of recent gradients to normalize the gradients. It deals with Adagrad's radically diminishing learning rates. It divides the learning rate by an exponentially decaying average of squared gradients. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about RMSProp Optimization Algorithm, Learning Rate, Leaky Averages and Momentum Method, Implementation of RMSProp from scratch, Gradient Descent Algorithm, Preconditioning and few more topics related to the same from here. I have presented the implementation of RMSProp Optimization Algorithm from scratch using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead!!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3cd7ad5baf1b.png)\n\n**Day131 of 300DaysOfData!**\n- **RMSProp Optimization Algorithm**: RMSProp is a gradient based optimization algorithm that utilizes the magnitude of recent gradients to normalize the gradients. It deals with Adagrad's radically diminishing learning rates. It divides the learning rate by an exponentially decaying average of squared gradients. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Adadelta Optimization Algorithms, Learning Rates, Leaky Averages, Momentum, Gradient Descent, Concise Implementation of Adadelta, Adam Optimization Algorithms, Vectorization and Minibatch SGD, Weighting Parameters, Normalization, Concise Implementation of Adam Algorithms and few more topics related to the same from here. I have presented the Implementation of Adadelta Optimization Algorithm and Adam Optimization Algorithm from scratch using PyTorch here in the Snapshot. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f28ebc8cc725.png)\n\n**Day132 of 300DaysOfData!**\n- **Adam Optimizer**: Adam uses exponential weighted moving averages also known as Leaky Averaging to obtain an estimate of both momentum and also the second moment of the gradient. It combines the features of many optimization algorithms. It uses EWMA on minibatch Stochastic Gradient Descent. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Adam and Yogi Optimization Algorithms, Variance, Minibatch SGD, Learning Rate Scheduling, Weight Vectors, Convolutional Layer, Linear Layer, Max Pooling Layer, Sequential API, RELU, Cross Entropy Loss, Schedulers, Overfitting and few more topics related to the same from here. I have presented the implementation of LeNet Architecture and Yogi Optimization Algorithm using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b7a6244a2a4c.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_68172c2da316.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fb1aeb9839c9.png)\n\n**Day133 of 300DaysOfData!**\n- **Adam Optimizer**: Adam uses exponential weighted moving averages also known as Leaky Averaging to obtain an estimate of both momentum and also the second moment of the gradient. It combines the features of many optimization algorithms. It uses EWMA on minibatch Stochastic Gradient Descent. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Learning Rate Scheduling, Square Root Scheduler, Factor Scheduler, Learning Rate and Polynomial Decay, Multi Factor Scheduler, Piecewise Constant, Optimization and Local Minimum, Cosine Scheduler and few more topics related to the same from here. I have presented the implementation of Multi Factor Scheduler and Cosine Scheduler using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_71e32b2b7c15.png)\n\n**Day134 of 300DaysOfData!**\n- **Adam Optimizer**: Adam uses exponential weighted moving averages also known as Leaky Averaging to obtain an estimate of both momentum and also the second moment of the gradient. It combines the features of many optimization algorithms. It uses EWMA on minibatch Stochastic Gradient Descent. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Model Computational Performance, Compilers and Interpreters, Symbolic Programming and Imperative Programming, Hybrid Programming, Dynamic Computations Graph, Hybrid Sequential, Acceleration by Hybridization, Multi Layer Perceptrons, Asynchronous Computation and few more topics related to the same from here. I have presented the implementation of Hybrid Sequential, Acceleration by Hybridization and Asynchronous Computation using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e007f1ad8fe8.png)\n\n**Day135 of 300DaysOfData!**\n- **Adam Optimizer**: Adam uses exponential weighted moving averages also known as Leaky Averaging to obtain an estimate of both momentum and also the second moment of the gradient. It combines the features of many optimization algorithms. It uses EWMA on minibatch Stochastic Gradient Descent. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Asynchronous Computation, Barriers and Blockers, Improving Computation and Memory Footprint, Automatic Parallelism, Parallel Computation and Communication, Training on Multiple GPUs, Splitting the Problem, Data Parallelism, Network Partitioning, Layer Wise Partitioning, Data Parallel Partitioning and few more topics related to the same from here. I have presented the implementation of Initializing Model Parameters and Defining LeNet Model using PyTorch here in the Snapshot. I am still working on the Implementation of LeNet Model. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Implementation of LeNet Architecture**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d48cfb755ebe.png)\n\n**Day136 of 300DaysOfData!**\n- **Adam Optimizer**: Adam uses exponential weighted moving averages also known as Leaky Averaging to obtain an estimate of both momentum and also the second moment of the gradient. It combines the features of many optimization algorithms. It uses EWMA on minibatch Stochastic Gradient Descent. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Training on Multiple GPUs, LeNet Architecture, Data Synchronization, Model Parallelism, Data Broadcasting, Data Distribution, Optimization Algorithms, Implementation Back Propagation, Model Animation, Cross Entropy Loss Function, Convolutional Layer, RELU Activation Function, Matrix Multiplication, Average Pooling Layer and few more topics related to the same from here. I have presented the implementation of Data Distribution, Data Synchronization and Training Function using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Implementation of LeNet Architecture**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3bc22fda0e6f.png)\n\n**Day137 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Optimization and Synchronization, ResNet Neural Networks Architecture, Convolutional Layer, Batch Normalization Layer, Strides and Padding, The Sequential API, Parameter Initialization and Logistics, Minibatch Gradient Descent, Training ResNet Model, Stochastic Gradient Descent Optimizer, Cross Entropy Loss Function, Back Propagation, Parallelization and few more topics related to the same from here. I have presented the implementation of ResNet Architecture, Initialization and Training the Model using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !! \n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Implementation of LeNet Architecture**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_82dd449fe949.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fa8de5903641.png)\n\n**Day138 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Computer Vision Applications, Image Augmentation, Deep Neural Networks, Common Image Augmentation Method such as Flipping and Cropping, Horizontal Flipping and Vertical Flipping, Changing the Color of Images, Overlying Multiple Image Augmentation Methods, CIFAR10 Dataset, Torch Vision Module and Random Color Jitter Instance and few more topics related to the same from here.  I have presented the Implementation of Flipping and Cropping the Images and Changing the Color of Images using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Implementation of LeNet Architecture**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b3e1876600c6.png)\n\n**Day139 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Image Augmentation, CIFAR10 Dataset, Using a Multi GPU Training Model, Fine Tuning the Model, Overfitting, Pretrain Neural Network, Target Initialization, ResNet Model, ImageNet Dataset, Normalization of RGB Images, Mean and Standard Deviation, Torch Vision Module, Flipping and Cropping Images, Adam Optimization, Cross Entropy Loss Function and few more topics related to the same from here. I have presented the implementation of Training the Model with Image Augmentation and Normalization of Images using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_54ea74e4d342.png)\n\n**Day140 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Fine Tuning the Model, Pretrain Neural Networks, Normalization of Images, Mean and Standard Deviation, Defining and Initializing the Model, Cross Entropy Loss Function, Data Loader Class, Learning Rate and Stochastic Gradient Descent, Model Parameters, Transfer Learning, Source Model and Target Model, Weights and Biases and few more topics related to the same from here. I have presented the implementation of Normalization of Images, Flipping and Cropping the Images and Training Pretrained Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cac521236aa2.png)\n\n**Day141 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Object Detection and Object Recognition, Image Classification and Computer Vision, Images and Bounding Boxes, Target Location and Axis Coordinates and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Regular Expressions, Disjunction, Grouping and Precedence, Precision and Recall, Substitution and Capture Groups, Lookahead Assertions, Words, Corpora and few more topics related to the same. I have presented the simple implementation of Object Detection and Bounding Boxes using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_16836a07f8af.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bd1e55bb248d.png)\n\n**Day142 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Computer Vision, Anchor Boxes, Object Detection Algorithms, Bounding Boxes, Generating Multiple Anchor Boxes, Computation Complexity, Sizes and Ratios and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Text Normalization, Unix Tools for Crude Tokenization and Normalization, Word Tokenization, Named Entity Detection, Penn Treebank Tokenization and few more topics related to the same from here. I have presented the implementation of Generating Anchor Boxes, Object Detection and Bounding Boxes using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_976037278080.png)\n\n**Day143 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Computer Vision, Generating Multiple Anchor Boxes, Batch Size, Coordinate Values, Intersection Over Union Algorithm, Jaccard Index, Computation Complexity, Sizes and Ratios and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Byte Pair Encoding Algorithm for Tokenization, Subword Tokens, Wordpiece and Greedy Tokenization Algorithm, Maximum Matching Algorithm, Word Normalization, Lemmatization and Stemming, The Porter Stemmer and few more Topics related to the same from here. I have presented the implementation of Generating Anchor Boxes and Intersection Over Union Algorithm using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_952306388867.png)\n\n**Day144 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Computer Vision, Labeling Training Set Anchor Boxes, Object Detection and Image Recognition, Ground Truth Bounding Box Index, Anchor Boxes and Offset Boxes, Intersection Over Union and Jaccard Algorithm and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Sentence Segmentation, The Minimum Edit Distance Algorithm, Viterbi Algorithm, N Gram Language Models, Probability, Spelling Correction and Grammatical Error Correction and few more topics related to the same from here. I have presented the implementation of Labeling Training Set Anchor Boxes and Initializing Offset Boxes using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ba8c09d28480.png)\n\n**Day145 of 300DaysOfData!**\n- **Image Segmentation**: Image Segmentation is the process of partitioning digital image into multiple segments or set of pixels. The goal of segmentation is to simplify the representation of image into something meaningful and easier to analyze. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Non Maximum Suppression Algorithms, Prediction Bounding Boxes, Ground Truth Bounding Boxes, Confidence Level, Batch Size, Intersection Over Union Algorithm or Jaccard Index, Aspect Ratios, Bounding Boxes for Prediction, Multi Box Target Function, Anchor Boxes and few more topics related to the same from here. I have presented the implementation of Initializing Multi Box Anchor Boxes and Initializing Prediction Bounding Boxes using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_25ff6bf79807.png)\n\n**Day146 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Multiscale Object Detection, Generating Multiple Anchor Boxes, Object Detection, Single Shot Multiple Detection Algorithm, Category Prediction Layer, Bounding Boxes Prediction Layer, Concatenating Predictions for Multiple Scales, Height and Width Down Sample Block, CNN Layer, RELU and Max Pooling Layer and few more topics related to the same from here. I have also spend some time reading the Book \"Speech and Language Processing\". Here, I have read about Part of Speech Tagging, Information Extraction, Named Entity Recognition, Regular Expressions and few more topics related to the same from here. I have presented the implementation of Initializing Category Prediction Layer and Height & Width Down Sample Block using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead!!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_22899a81c6ca.png)\n\n**Day147 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Single Shot Multi Box Detection Algorithm, The Base Neural Network, Height Width Down Sample Block, Category Prediction Layer, Bounding Box Prediction Layer, Multiscale Feature Blocks, The Sequential API and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about N Gram Language Models, Chain Rule of Probability, Markov Models, Maximum Likelihood Estimation, Relative Frequency, Evaluating Language Models, Log Probabilities, Perplexity, Generalization & Zeros, Sparsity and few more topics related to the same from here. I have presented the implementation of Base SSD Network and Complete SSD Model using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead!!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_28416e7caffc.png)\n\n**Day148 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Single Shot Multi Box Detection Model, Implementation of Tiny SSD Model, Forward Propagation Function, Data Reading and Initialization, Object Detection, Multi Scale Feature Block, Global Max Pooling Layer and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Unknown Words or Out of Vocabulary Words, OOV Rate, Smoothing, Laplace Smoothing, Text Classification, Add One Smoothing, MLE, Add K Smoothing and few more topics related to the same from here. I have presented the implementation of Single Shot Multi Box Detection Model and Dataset Initialization using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0cf4519cb91f.png)\n\n**Day149 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Softmax Activation Function, Convolutional Layer, Training the Single Shot Multi Box Detection Model, Multi Scale Anchor Boxes, Cross Entropy Loss Function, L1 Normalization Loss Function, Average Absolute Error, Accuracy Rate, Category and Offset Losses and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Backoff and Interpolation, Katz Backoff, Kneser Ney Smoothing, Absolute Discounting, The Web and Stupid Backoff, Perplexity Relation to Entropy and few more topics related to the same from here. I have presented the implementation of Training Single Shot Multi Box Detection Model, Loss and Evaluation Functions using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cc5ac2fe4b31.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eee396d2e433.png)\n\n**Day150 of 300DaysOfData!**\n- **Image Segmentation**: Image Segmentation is the process of partitioning digital image into multiple segments or set of pixels. The goal of segmentation is to simplify the representation of image into something meaningful and easier to analyze. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Region Based Convolutional Neural Networks, Fast RCNN, Faster RCNN, Mask RCNN, Category Prediction Layer, Bounding Boxes Prediction Layer, Support Vector Machines, Rol Pooling Layer and Rol Alignment Layer, Pixel Level Semantics, Image Segmentation and Instance Segmentation, Pascal VOC2012 Semantic Segmentation, RGB, Data Preprocessing and few more topics related to the same from here. I have presented the implementation of Semantic Segmentation and Data Preprocessing using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_134cabc34fa6.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9a7199ea0d29.png)\n\n**Day151 of 300DaysOfData!**\n- **Sequence to Sequence Model**: Sequence to Sequence Neural Networks can be built with a modular and reusable Encoder and Decoder Architecture. The Encoder Model generates a Thought Vector which is a Dense and fixed Dimension Vector representation of the Data. The Decoder Model use Thought Vectors to generate Output Sequences. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Dataset Classes for Custom Semantic Segmentation, RGB Channels, Normalization of Images, Random Cropping Operation, Sequence to Sequence Recurrent Neural Networks, Label Encoder, One Hot Encoder, Encoding and Vectorization, Long Short Term Memory or LSTM and few more topics related to the same from here. I have presented the implementation Dataset Classes for Custom Semantic Segmentation using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8f6f99e3986c.png)\n\n**Day152 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Transposed Convolutional Layer, CNNs, Basic 2D Transposed Convolution, Broadcasting Matrices, Kernel Size, Padding, Strides and Channels, Analogy to Matrix Transposition, Matrix Multiplication and Matrix Vector Multiplication and few more topics related to the same from here.  I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Naive Bayes and Sentiment Classification, Text Categorization, Spam Detection, Probabilistic Classifier, Multinomial NB Classifier, Bag of Words, MLP, Unknown and Stop Words and few more topics related to the same from here. I have presented the implementation of Transposed Convolution, Padding, Strides and Matrix Multiplication using PyTorch here in the Snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6ba92a7f2b98.png)\n\n**Day153 of 300DaysOfData!**\n- **Transposed Convolution**: Transposed Convolution implies that Stride & Padding do not correspond to the number of zeros added around the image and the amount of shift in the kernel when sliding it across the input as they would in a standard convolution operation. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Fully Convolutional Neural Networks, Semantic Segmentation Principles, Transposed Convolutional Layer, Constructing a Pretrained Neural Networks Model, Global Average Pooling Layer, Flattening Layer, Image Processing and Upsampling, Bilinear Interpolation Kernel Function and few more topics related to the same from here. I have presented the implementation of Fully Convolutional Layer, Pretrained NNs, Bilinear Interpolation Kernel Function and Transposed Convolutional Layer using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_86cf46eade47.png)\n\n**Day154 of 300DaysOfData!**\n- **Neural Style Transfer Algorithms**: It is the task of changing the style of an image in one domain to the style of an image in another domain. It manipulates images or videos in order to adopt the appearance of another image. On my Journey of Machine Learning and Deep Learning, Today I have read and Implemented from the Book **Dive into Deep Learning**. Here, I have learned about Softmax Cross Entropy Loss Function, Stochastic Gradient Descent, CNNs, Neural Networks Style Transfer, Composite Images, RGB Channels, Normalization and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Optimizing Naive Bayes for Sentiment Analysis, Sentiment Lexicons, Naive Bayes as Language Models, Precision, Recall and FMeasure, Multi Label and Multinomial Classification and few more topics related to the same from here. I have started working on Style Transfer using Neural Networks. The Notebook is mentioned below though I am still working on it.\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [**Neural Networks Style Transfer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3cb87f021996.png)\n\n**Day155 of 300DaysOfData!**\n- **Neural Style Transfer Algorithms**: It is the task of changing the style of an image in one domain to the style of an image in another domain. It manipulates images or videos in order to adopt the appearance of another image. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Neural Networks Style Transfer, Convolutional Neural Networks, Reading the Content and Style Images, Preprocessing and Postprocessing the Images, Extracting Image Features, Composite Images, VGG Neural Networks, Squared Error Loss Faction, Total Variance Loss Function, Normalization of RGB Channels of Images and few more topics related to the same from here. I am still working on Style Transfer using Neural Networks. The Notebook is mentioned below though I am still working on it. I have presented the implementation of Function for Extracting Features and Square Error Loss Function using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [**Neural Networks Style Transfer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6aeef59b0e44.png)\n\n**Day156 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Creating and Initializing the Composite Images, Synchronization Functions, Adam Optimizer, Gram Matrix, Convolutional Neural Networks, Neural Networks Style Transfer, Loss Functions and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Test Sets and Cross Validation, Statistical Significance Testing, Naive Bayes Classifiers, Bootstrapping, Logistic Regression, Generative and Discriminative Classifiers, Feature Representation, Sigmoid Classification, Weight and Bias Term and few more topics related to the same from here. I have completed working on Style Transfer using Neural Networks. The Notebook is mentioned below but I am still updating.\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [**Neural Networks Style Transfer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8493ad10818f.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_968ea215a74a.png)\n\n**Day157 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Computer Vision, Image Classification, CIFAR10 Dataset, Obtaining and Organizing the Dataset, Augmentation and few more topics related to the same. Apart from that, I have learned about Data Scraping and Scrapy, Named Entity Recognition and SpaCy, Trained Transformer Model using SpaCy, Geocoding and few more topics related to the same from here. I have completed working on Style Transfer using Neural Networks Notebook. I have started working on Object Recognition on Images: CIFAR10 Notebook. All the Notebooks are mentioned below. I have presented the implementation of Obtaining and Organizing the CIFAR10 Dataset here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned above and below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Neural Networks Style Transfer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n  - [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_45df04bc7838.png)\n\n**Day158 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Computer Vision, Image Classification, Image Augmentation and Overfitting, Normalization of RGB Channels, Data Loader and Validation Set and few more topics related to the same from here. Apart from that, I have learned about Stanford NER Algorithms, NLTK, Named Entity Recognition and few more topics related to the same. I have completed working on Style Transfer using Neural Networks Notebook. I have started working on Object Recognition on Images: CIFAR10 Notebook. All the Notebooks are mentioned below. I have presented the implementation of Obtaining and Organizing the Dataset, Image Augmentation and Normalization using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Neural Networks Style Transfer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n  - [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dfb2bac7330a.png)\n\n**Day159 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Computer Vision, ResNet Model and Residual Blocks, Xavier Random Initialization, Cross Entropy Loss Function, Defining Training Functions, Stochastic Gradient Descent, Learning Rate Scheduler, Evaluation Metrics and few more topics related to the same. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Sentiment Classification, Learning in Logistic Regression, Conditional MLE, Cost Function and few more topics related to the same from here. I am working on Object Recognition on Images: CIFAR10 Notebook. The Notebook is mentioned below. I have presented the implementation Defining a Training Function using PyTorch here in the Snapshot.  I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_70b427428b74.png)\n\n**Day160 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about ImageNet Dataset, Obtaining and Organizing the Dataset, Image Augmentation such as Flipping and Resizing the Image, Changing Brightness and Contrast of Image, Transfer Learning and Features, Normalization of Images and few more topics related to the same from here. I have completed working on Object Recognition on Images: CIFAR10 Notebook. I have started working on Dog Breed Identification: ImageNet Notebook. All the Notebooks are mentioned below. I have presented the implementation of Image Augmentation and Normalization, Defining Neural Networks Model and Loss Function using PyTorch here in the Snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n  - [**Dog Breed Identification: ImageNet**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e6aaa60976b9.png)\n\n**Day161 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Defining the Training Functions, Computer Vision, Hyperparameters, Stochastic Gradient Descent Optimization Function, Learning Rate Scheduler and Optimization, Training Loss and Validation Loss and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Gradient for Logistic Regression, SGD Algorithm, Minibatch Training and few more topics related to the same from here. I am working on Dog Breed Identification: ImageNet Notebook. The Notebooks is mentioned below. I have presented the implementation of Defining the Training Function using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead!!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n  - [**Dog Breed Identification: ImageNet**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0b985eec3814.png)\n\n**Day162 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Pretrained Text Representations, Word Embedding and Word2Vec, One Hot Vectors, The Skip Gram Model and Training, The Continuous Bag of Words Model and Training, Approximate Training, Negative Sampling, Hierarchical Softmax, Reading and Processing the Dataset, Subsampling, Vocabulary and few more topics related to the same from here. Apart from that, I have also read about Improving Chemical Autoencoders Latent Space and Molecular Diversity with Hetero Encoders. I am working on Dog Breed Identification: ImageNet Notebook. The Notebooks is mentioned below. I have presented the implementation of Reading and Preprocessing the Dataset, Subsampling and Comparison using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n  - [**Dog Breed Identification: ImageNet**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_017633f931fc.png)\n\n**Day163 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Subsampling, Extracting Central Target Words and Context Words, Maximum Context Window Size, Penn Tree Bank Dataset and Pretraining Word Embedding and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Regularization and Overfitting, Manhattan Distance, Lasso and Ridge Regression, Multinomial Logistic Regression, Features in MLR, Learning in MLR, Interpreting Models, Deriving Gradient Equation and few more topics related to the same from here. I have completed working on Dog Breed Identification: ImageNet Notebook. I have presented the implementation of Extracting Central Target Words and Context Words using PyTorch here in the snapshot. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [**Object Recognition on Images: CIFAR10**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n  - [**Dog Breed Identification: ImageNet**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8066b1d59975.png)\n\n**Day164 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Subsampling and Negative Sampling, Word Embedding and Word2Vec, Probability, Reading into Batches, Concatenation and Padding, Random Minibatches and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Vector Semantics and Embeddings, Lexical Semantics, Lemmas and Senses, Word Sense Disambiguation, Word Similarity, Principle of Contrast, Representation Learning, Synonymy and few more topics related to the same from here. I have presented the implementation Negative Sampling using PyTorch here in the snapshot. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ce9f7edf1b0c.png)\n\n**Day165 of 300DaysOfData!**\n- **Subsampling**: Subsampling is a method that reduces data size by selecting a subset of the original data. The subset is specified by choosing a parameter. Subsampling attempts to minimize the impact of high frequency words on the training of a word embedding model. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Word Embedding, Batches, Loss Function and Padding, Center and Context Words, Negative Sampling, Data Loader Instance, Vocabulary, Subsampling, Data Iterations, Mask Variables and few more topics related to the same from here. I have presented the implementation of Reading Batches and Function for Loading PTB Dataset using PyTorch here in the snapshots. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !! \n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dfb0f379ea0a.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_23204f14d9a8.png)\n\n**Day166 of 300DaysOfData!**\n- **Word Embedding**: Word Embedding is a term used for the representation of words for text analysis typically in the form of a real valued vector that encodes the meaning of the word such that the words that are closer in the vector space are expected to be similar in meaning. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Word Embedding, Word2Vec, The Skip Gram Model, Embedding Layer, Word Vector, Skip Gram Model Forward Calculation, Batch Matrix Multiplication, Binary Cross Entropy Loss Function, Negative Sampling, Mask Variables and Padding, Initializing Model Parameters and few more topics related to the same from here. I have presented the implementation of Embedding Layer, Skip Gram Model Forward Calculation and Binary Cross Entropy Loss Function using PyTorch here in the snapshot. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4f61240b91a6.png)\n\n**Day167 of 300DaysOfData!**\n-  On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Training Skip Gram Model, Loss Function, Applying Word Embedding Model, Negative Sampling, Word Embedding with Global Vectors or Glove, Conditional Probability, The Glove Model, Cross Entropy Loss Function and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Word Relatedness, Semantic Field, Semantic Frames and Roles, Connotation and Sentiment, Vector Semantics, Embeddings and few more topics related to the same from here. I have presented the implementation of Training Word Embedding Model using PyTorch here in the snapshot. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6066b8e18fae.png)\n\n**Day168 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Subword Embedding, Fast Text and Byte Pair Encoding, Finding Synonyms and Analogies, Pretrained Word Vectors, Token Embedding, Central Words and Context Words and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Words and Vectors, Vectors and Documents, Term Document Matrices, Information Retrieval, Row Vector and Context Matrix and few more topics related to the same from here.  I have presented the implementation of Defining Token Embedding Class using PyTorch here in the snapshot. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_95c9c97c985f.png)\n\n**Day169 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Finding Synonyms and Analogies, Word Embedding Model and Word2Vec, Applying Pretrained Word Vectors, Cosine Similarity and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have read about Cosine for measuring similarity, Dot and Inner Products, Weighing terms in the vector, Term Frequency Inverse Document Frequency or TFIDF, Collection Frequency, Applications of TFIDF Vector Model and few more topics related to the same from here.  I have presented the implementation of Cosine Similarity and Finding Synonyms and Analogies using PyTorch here in the snapshot. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a047395bc9f6.png)\n\n**Day170 of 300DaysOfData!**\n- **Bidirectional Encoder Representations from Transformers**: ELMO encodes context bidirectionally but uses task specific architectures and GPT is a task agnostic but encodes context left to right. BERT encodes context bidirectionally and requires minimal architecture changes for a wide range of NLP tasks. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about BERT Architecture, From Context Independent to Context Sensitive, Word Embedding Model and Word2Vec, From Task Specific to Task Agnostic, Embeddings from Language Models or ELMO Architecture, Input Representations, Token, Segment and Positional Embedding and Learnable Positional Embedding and few more topics related to the same from here.  I have presented the implementation of BERT Input Representations and BERT Encoder Class using PyTorch here in the snapshot. I hope you will gain some insights. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dd20a307f3a0.png)\n\n**Day171 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about BERT Encoder Class, Pretraining Tasks, Masked Language Modeling, Multi Layer Perceptron, Forward Inference, BERT Input Sequences, Bidirectional Context Encoding and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have learned about Pointwise Mutual Information or PMI, Laplace Smoothing, Word2Vec, Skip Gram with Negative Sampling or SGNS, The Classifier, Logistic and Sigmoid Function, Cosine Similarity and Dot Product and few more topics related to the same from here. I have presented the implementation of Masked Language Modeling and BERT Encoder using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3725f9012f24.png)\n\n**Day172 of 300DaysOfData!**\n- **Bidirectional Encoder Representations from Transformers**: ELMO encodes context bidirectionally but uses task specific architectures and GPT is a task agnostic but encodes context left to right. BERT encodes context bidirectionally and requires minimal architecture changes for a wide range of NLP tasks. The embeddings are the sum of the Token, Segment and Positional Embeddings. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Bidirectional Encoder Representations from Transformers or BERT Architecture, Next Sentence Prediction Model, Cross Entropy Loss Function, MLP, BERT Model, Masked Language Modeling, BERT Encoder, Pretraining BERT Model and few more topics related to the same from here. I have presented the implementation of Next Sentence Prediction and BERT Model using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f524168b84ff.png)\n\n**Day173 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Pretraining BERT Model and Dataset, Defining Helper Functions for Pretraining Tasks, Generating Next Sentence Prediction Task, Generating Masked Language Modeling Task, Sequence Tokens and few more topics related to the same from here. I have also spend some time reading the Book **Speech and Language Processing**. Here, I have read about Learning Skip Gram Embeddings, Binary Classifier, Target and Context Embedding, Visualizing Embeddings, Semantic Properties of Embeddings and few more topics related to the same from here. I have presented the implementation of Generating Next Sentence Prediction Task and Generating Masked Language Modeling Task using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Speech and Language Processing**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e1ea30a7ae19.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_13a5f0c0b928.png)\n\n**Day174 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Pretraining BERT Model, Next Sentence Prediction Task and Masked Language Modeling Task, Transforming Text into Pretraining Dataset and few more topics related to the same from here. I have also learned about Scorer and Example Instances of SpaCy Model, Long Short Term Memory Neural Networks, Smiles Vectorizer, Feed Forward Neural Networks and few more topics related to the same. I have presented the implementation of Transforming Text into Pretraining Dataset using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_167c143310f2.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0c30a366455a.png)\n\n**Day175 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Pretraining BERT Model, Cross Entropy Loss Function, Adam Optimization Function, Zeroing Gradients, Back Propagation and Optimization, Masked Language Modeling Loss and Next Sentence Prediction Loss and few more topics related to the same from here. I have presented the implementation of Pretraining BERT Model, Getting Loss from BERT Model and Training a Neural Networks Model using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_257f8f5b09ea.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9217d6be95bf.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_92543bee83dd.png)\n\n**Day176 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Natural Language Processing Applications, NLP Architecture and Pretraining, Sentiment Analysis and Dataset, Text Classification, Tokenization and Vocabulary, Padding Tokens to Same Length and few more topics related to the same from here. Apart from that I have also learned about Named Entity Recognition, Frequency Distribution, NLTK, Extending Lists and few more topics related to the same from here. I have presented the implementation of Reading the Dataset, Tokenization and Vocabulary and Padding to Fixed Length using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Sentiment Analysis Dataset Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_78434161689b.png)\n\n**Day177 of 300DaysOfData!**\n- **Sentiment Analysis**: Sentiment Analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify and study affective states and subjective information. It is widely applied to voice of the customer materials such as reviews and survey responses, online and social media and healthcare materials for applications that range from marketing to customer service to clinical medicine. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Creating Data Iterations, Tokenization and Vocabulary, Truncating and Padding, Recurrent Neural Networks Model and Sentiment Analysis, Pretrained Word Vectors and Glove, Bidirectional LSTM and Embedding Layer, Linear Layer and Decoding, Encoding and Sequence Data, Xavier Initialization and few more topics related to the same from here. I have presented the implementation of Bidirectional Recurrent Neural Networks Model using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Sentiment Analysis Dataset Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n  - [**Sentiment Analysis with RNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eff594a46f52.png)\n\n**Day178 of 300DaysOfData!**\n- **Sentiment Analysis**: Sentiment Analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify and study affective states and subjective information. It is widely applied to voice of the customer materials such as reviews and survey responses, online and social media and healthcare materials for applications that range from marketing to customer service to clinical medicine. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Word Vectors and Vocabulary, Training and Evaluating Bidirectional RNN Model, Sentiment Analysis and One Dimensional Convolutional Neural Networks, One Dimensional Cross Correlation Operation, Max Over Time Pooling Layer, The Text CNN Model, RELU Activation Function and Dropout Layer and few more topics related to the same from here. I have presented the implementation of Text Convolutional Neural Networks using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Sentiment Analysis Dataset Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n  - [**Sentiment Analysis with RNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)\n  - [**Sentiment Analysis with CNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bae991b5be2d.png)\n\n**Day179 of 300DaysOfData!**\n- **Natural Language Inference**: Natural Language Inference is a study where a hypothesis can be inferred from a premise where both are a text sequence. It determines the logical relationship between a pair of text sequences. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Natural Language Inference and Dataset, Premise, Hypothesis or Entailment, Contradiction and Neutral, The Stanford Natural Language Inference Dataset, Reading SNLI Dataset and few more topics related to the same from here. I have presented the implementation of Reading SNLI Dataset using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Sentiment Analysis Dataset Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n  - [**Sentiment Analysis with RNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)\n  - [**Sentiment Analysis with CNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb)\n  - [**Natural Language Inference Dataset**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b1e9c72a5c2e.png)\n\n**Day180 of 300DaysOfData!**\n- **Natural Language Inference**: Natural Language Inference is a study where a hypothesis can be inferred from a premise where both are a text sequence. It determines the logical relationship between a pair of text sequences. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Natural Language Inference and SNLI Dataset, Premises, Hypotheses and Labels, Vocabulary, Padding and Truncation of Sequences, Dataset and DataLoader Module and few more topics related to the same from here. Apart from here, I have also read about Confusion Matrix and Classification Reports, Frequency Distribution and Word Cloud of Text Data. I have presented the implementation of Loading SNLI Dataset using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Sentiment Analysis Dataset Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n  - [**Sentiment Analysis with RNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)\n  - [**Sentiment Analysis with CNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb)\n  - [**Natural Language Inference Dataset**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ccdf6f2ee3eb.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c1c01d44ae3e.png)\n\n**Day181 of 300DaysOfData!**\n- **Natural Language Inference**: Natural Language Inference is a study where a hypothesis can be inferred from a premise where both are a text sequence. It determines the logical relationship between a pair of text sequences. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Natural Language Inference using Attention Model, Multi Layer Perceptron or MLP with Attention Mechanisms, Alignment of Premises and Hypotheses, Word Embeddings and Attention Weights and few more topics related to the same from here. I have presented the implementation of MLP and Attention Mechanism using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Natural Language Inference Dataset**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n  - [**Natural Language Inference**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8aaca1f175f7.png)\n\n**Day182 of 300DaysOfData!**\n- **Comparing and Aggregating Class**: Comparing Class compares a word in one sequence with the other sequence that is softly aligned with the word. Aggregating Class aggregates the two sets of comparison vectors to infer the logical relationship. It feeds the concatenation of both summarization results into MLP function to obtain the classification result of the logical relationship. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Comparing Word Sequences, Soft Alignment, Multi Layer Perceptron or MLP Classifier, Aggregating Comparison Vectors, Linear Layer and Concatenation, Decomposable Attention Model, Embedding Layer and few more topics related to the same from here. I have presented the implementation of Comparing Class, Aggregating Class and Decomposable Attention Model using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Natural Language Inference Dataset**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n  - [**Natural Language Inference**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9bae4b6367af.png)\n\n**Day183 of 300DaysOfData!**\n- **Comparing and Aggregating Class**: Comparing Class compares a word in one sequence with the other sequence that is softly aligned with the word. Aggregating Class aggregates the two sets of comparison vectors to infer the logical relationship. It feeds the concatenation of both summarization results into MLP function to obtain the classification result of the logical relationship. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Decomposable Attention Model, Embedding Layer and Linear Layer, Training and Evaluating the Attention Model, Natural Language Inference, Entailment, Contradiction and Neutral, Pretrained Glove Embedding, SNLI Dataset, Adam Optimizer and Cross Entropy Loss Function, Premises and Hypotheses and few more topics related to the same from here. I have presented the implementation of Training and Evaluating Attention Model using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Natural Language Inference Dataset**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n  - [**Natural Language Inference: Attention**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3f1029beee25.png)\n\n**Day184 of 300DaysOfData!**\n- **BERT Model Notes**: BERT requires minimal architecture changes for sequence level and token level NLP applications such as Single Text Classification, Text Pair Classification or Regression and Text Tagging. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Fine Tuning BERT for Sequence Level and Token Level Applications, Single Text Classification, Text Pair Classification or Regression, Text Tagging, Question Answering, Natural Language Inference and Pretrained BERT Model, Loading Pretrained BERT Model and Parameters, Semantic Textual Similarity, POS Tagging and few more topics related to the same from here. I have presented the implementation of Loading Pretrained BERT Model and Parameters using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Natural Language Inference: Attention**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n  - [**Natural Language Inference: BERT**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8894a89f51d6.png)\n\n**Day185 of 300DaysOfData!**\n- **BERT Model Notes**: BERT requires minimal architecture changes for sequence level and token level NLP applications such as Single Text Classification, Text Pair Classification or Regression and Text Tagging. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Loading Pretrained BERT Model and Parameters, The Dataset for Fine Tuning BERT Model, Premise, Hypothesis and Input Sequence, Tokenization and Vocabulary, Truncating and Padding Tokens, Natural Language Inference and few more topics related to the same from here. I have presented the implementation of The Dataset for Fine Tuning BERT Model and Generating Training and Test Examples using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Natural Language Inference: Attention**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n  - [**Natural Language Inference: BERT**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4e909b016c37.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0a3e266361df.png)\n\n**Day186 of 300DaysOfData!**\n- **Generative Adversarial Networks**: Generative Adversarial Networks consist of two deep networks Generator and Discriminator. The Generator generates the image as much closer to the true image as possible to fool Discriminator by maximizing the cross entropy loss. The Discriminator tries to distinguish the generated images from the true images by minimizing the cross entropy loss. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Generative Adversarial Networks, Generator and Discriminator Networks, Updating Discriminator and few more topics related to the same from here. I have also read about Recommender Systems, Collaborative Filtering, Explicit and Implicit Feedbacks, Recommendation Tasks and few more topics related to the same. I have presented a simple implementation of Generator and Discriminator Networks and Optimization using PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Natural Language Inference: Attention**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n  - [**Natural Language Inference: BERT**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_80a238c82505.png)\n\n**Day187 of 300DaysOfData!**\n- **Generative Adversarial Networks**: Generative Adversarial Networks consist of two deep networks Generator and Discriminator. The Generator generates the image as much closer to the true image as possible to fool Discriminator by maximizing the cross entropy loss. The Discriminator tries to distinguish the generated images from the true images by minimizing the cross entropy loss. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Generator and Discriminator Networks, Binary Cross Entropy Loss Function, Adam Optimizer and Normalized Tensors, Gaussian Distribution, Real and Generated Data and few more topics related to the same from here. I have presented a simple implementation of Updating Generator and Training Function using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Natural Language Inference: Attention**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n  - [**Natural Language Inference: BERT**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ddf0c1fa199.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6b11c4728d1e.png)\n\n**Day188 of 300DaysOfData!**\n- **Generative Adversarial Networks**: Generative Adversarial Networks consist of two deep networks Generator and Discriminator. The Generator generates the image as much closer to the true image as possible to fool Discriminator by maximizing the cross entropy loss. The Discriminator tries to distinguish the generated images from the true images by minimizing the cross entropy loss. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Deep Convolutional Generative Adversarial Networks, The Pokemon Dataset, Resizing and Normalization, DataLoader, The Generator Block Module, Transposed Convolution Layer, Batch Normalization Layer, RELU Activation Function and few more topics related to the same from here. I have also read about Inter Quartile Range, Mean Absolute Deviation, Box Plots, Density Plots, Frequency Tables and few more topics related to the same. I have presented the implementation of The Generator Block and Pokemon Dataset using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the Topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Deep Convolutional GAN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_38abccbc58a3.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_283d63c2aa75.png)\n\n**Day189 of 300DaysOfData!**\n- **Generative Adversarial Networks**: Generative Adversarial Networks consist of two deep networks Generator and Discriminator. The Generator generates the image as much closer to the true image as possible to fool Discriminator by maximizing the cross entropy loss. The Discriminator tries to distinguish the generated images from the true images by minimizing the cross entropy loss. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Deep Convolutional Generative Adversarial Networks, The Generator and The Discriminator Networks, Leaky RELU Activation Function and Dying RELU Problem, Batch Normalization, Convolutional Layer, Stride and Padding and few more topics related to the same from here. I have presented the implementation of The Discriminator Block and The Generator Block using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Deep Convolutional GAN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7490db015ce5.png)\n\n**Day190 of 300DaysOfData!**\n- **Generative Adversarial Networks**: Generative Adversarial Networks consist of two deep networks Generator and Discriminator. The Generator generates the image as much closer to the true image as possible to fool Discriminator by maximizing the cross entropy loss. The Discriminator tries to distinguish the generated images from the true images by minimizing the cross entropy loss. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the Book **Dive into Deep Learning**. Here, I have learned about Deep Convolutional Generative Adversarial Networks, The Generator and The Discriminator Blocks, Cross Entropy Loss Function, Adam Optimization Function and few more topics related to the same from here. I have presented the implementation of Training Generator and Discriminator Networks using PyTorch here in the snapshots. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - [**Dive into Deep Learning**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**Deep Convolutional GAN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ddc226ca43c5.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c04e0d27c9cf.png)\n\n**Day191 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, Today I have started reading and implementing from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Deep Learning in Practice, Areas of Deep Learning, A Brief History of Neural Networks, Fastai and Jupyter Notebooks, Cat and Dog Classification, Image Loaders, Pretrained Models, RESNET and CNNs, Error Rate and few more topics related to the same from here. I have presented the implementation of Cat and Dog Classification using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Introduction Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98c880dcab5b.png)\n\n**Day192 of 300DaysOfData!**\n- **Transfer Learning**: Transfer Learning is defined as the process of using pretrained model for a task different from what it was originally trained for. Fine Tuning is a transfer learning technique that updates the parameters of pretrained model by training for additional epochs using a different task from that used for pretraining. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Machine Learning and Weight Assignment, Neural Networks and Stochastic Gradient Descent, Limitations Inherent to ML, Image Recognition, Classification and Regression, Overfitting and Validation Set, Transfer Learning, Semantic Segmentation, Sentiment Classification, Data Loaders and few more topics related to the same from here. I have presented the implementation of Semantic Segmentation and Sentiment Classification using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Introduction Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cd1fdeccb560.png)\n\n**Day193 of 300DaysOfData!**\n- **Transfer Learning**: Transfer Learning is defined as the process of using pretrained model for a task different from what it was originally trained for. Fine Tuning is a transfer learning technique that updates the parameters of pretrained model by training for additional epochs using a different task from that used for pretraining. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Tabular Data and Classification, Tabular Data Loaders, Categorical and Continuous Data, Recommendation System and Collaborative Filtering, Datasets for Models, Validation Sets and Test Sets, Judgement in Test Sets and few more topics related to the same from here. I have presented the implementation of Tabular Classification and Recommendation System Model using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Introduction Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e20d40852ea5.png)\n\n**Day194 of 300DaysOfData!**\n- **The Drivetrain Approach**: It can be stated as start with considering your objective then think about what actions you can take to meet that objective and what data you have or can acquire that can help and then build a model that you can use to determine the best actions to take to get the best results in terms of your objective. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about The Practice of Deep Learning, The State of DL, Computer Vision, Text and NLP, Combining Text and Images, Tabular Data and Recommendation Systems, The Drivetrain Approach, Gathering Data and Duck Duck Go, Questionnaire and few more topics related to the same from here. I have presented the implementation of Gathering Data for Object Detection using Duck Duck Go and Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Detection**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2f725981c5c2.png)\n\n**Day195 of 300DaysOfData!**\n- **The Drivetrain Approach**: It can be stated as start with considering your objective then think about what actions you can take to meet that objective and what data you have or can acquire that can help and then build a model that you can use to determine the best actions to take to get the best results in terms of your objective. On my Journey of Machine Learning and Deep Learning, Today I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have Fastai Dependencies and Functions, Biased Dataset, Data to Data Loaders, Data Block API, Dependent and Independent Variables, Random Splitting, Image Transformations and few more topics related to the same from here. I have presented the implementation of Gathering Data and Initializing Data Loaders using Duck Duck Go and Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Detection**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5130330e59fd.png)\n\n**Day196 of 300DaysOfData!**\n- **Data Augmentation**: Data Augmentation refers to creating random variations of the input data such that they appear different but do not change the meaning of the data. RandomResizedCrop is a specific example of Data Augmentation. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Data Loaders, Image Block, Resizing, Squishing and Stretching Images, Padding Images, Data Augmentation, Image Transformations, Training the Model and Error Rate, Random Resizing and Cropping and few more topics related to the same from here. I have presented the implementation of Data Loaders, Data Augmentation and Training the Model using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Detection**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7fa2566ab205.png)\n\n**Day197 of 300DaysOfData!**\n- **Data Augmentation**: Data Augmentation refers to creating random variations of the input data such that they appear different but do not change the meaning of the data. RandomResizedCrop is a specific example of Data Augmentation. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Training Pretrained Model, Data Augmentation and Transformations, Classification Interpretation and Confusion Matrix, Cleaning Dataset, Inference Model and Parameters, Notebooks and Widgets and few more topics related to the same from here. I have presented the implementation of Classification Interpretation, Cleaning Dataset, Inference Model and Parameters using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Detection**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5be9a2457236.png)\n\n**Day198 of 300DaysOfData!**\n- **Data Ethics**: Ethics refers to well founded standards of right and wrong that prescribe what humans should do. It is the study and development of ones ethical standards. Recourse Process, Feedback Loops, Bias are key examples for Data Ethics. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Data Ethics, Bugs and Recourse, Feedback Loops, Bias, Integrating ML with Product Design, Training a Digit Classifier, Pixels and Computer Vision, Tenacity and Deep Learning, Pixel Similarity, List Comprehensions and few more topics related to the same from here. I have presented the simple implementation of Pixels and Computer Vision using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c3f25089d26a.png)\n\n**Day199 of 300DaysOfData!**\n- **L1 and L2 Norm**: Taking the mean of absolute value of differences is called Mean Absolute Difference or L1 Norm. Taking the mean of square of differences and then taking the square root is called Root Mean Squared Error or L2 Norm. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Rank of Tensors, Mean Absolute Difference or L1 Norm and Root Mean Squared Error or L2 Norm, Numpy Arrays and PyTorch Tensors, Computing Metrics using Broadcasting and few more topics related to the same from here. I have presented the simple implementation of Arrays and Tensors, L1 and L2 Norm using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6262ed4e270e.png)\n\n**Day200 of 300DaysOfData!**\n- **L1 and L2 Norm**: Taking the mean of absolute value of differences is called Mean Absolute Difference or L1 Norm. Taking the mean of square of differences and then taking the square root is called Root Mean Squared Error or L2 Norm. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Computing Metrics using Broadcasting, Mean Absolute Error, Stochastic Gradient Descent, Initializing Parameters, Loss Function, Calculating Gradients, Backpropagation and Derivatives, Learning Rate Optimization and few more topics related to the same from here. I have presented the simple implementation of Stochastic Gradient Descent using Fastai here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fe06b9bfab42.png)\n\n**Day201 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about The Gradient Descent Process, Initializing Parameters, Calculating Predictions and Inspecting, Calculating Loss and MSE, Calculating Gradients and Backpropagation, Stepping the Weights and Updating Parameters, Repeating the Process & Stopping the Process and few more topics related to the same from here. I have presented the implementation of The Gradient Descent Process using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6f1d7e5c54f1.png)\n\n**Day202 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about The MNIST Loss Function, Matrices and Vectors, Independent Variables, Weights and Biases, Parameters, Matrix Multiplication and Dataset Class, Gradient Descent Process and Learning Rate, Activation Function and few more topics related to the same from here. I have presented the implementation of The Dataset Class and Matrix Multiplication using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_10b2699df2b0.png)\n\n**Day203 of 300DaysOfData!**\n- **Accuracy and Loss Function**: The key difference between metric such as accuracy and loss function is that the loss is to drive automated learning and the metric is to drive human understanding. The loss must be a function with meaningful derivative and metrics focuses on performance of the model. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Matrix Multiplication, Activation Function, Loss Function, Gradients and Slope, Sigmoid Function, Accuracy Metrics and Understanding and few more topics related to the same from here. I have presented the implementation of Loss Function and Sigmoid using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ebc7f3651236.png)\n\n**Day204 of 300DaysOfData!**\n- **SGD and Minibatches**: The process to change or update the weights based on the gradients in order to consider some of the details involved in the next phase of the learning process is called an Optimization Step. The calculation of average loss for a few data items at a time is called a Minibatch. The number of data items in the Minibatch is called Batchsize. A larger Batchsize means more accurate and stable estimate of the dataset gradients from the loss function whereas a single Batchsize result in an imprecise and unstable gradient. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Stochastic Gradient Descent and Minibatches, Optimization Step, Batch Size, DataLoader and Dataset, Initializing Parameters, Weights and Bias, Backpropagation and Gradients, Loss Function and few more topics related to the same from here. I have presented the implementation of DataLoader and Gradients using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1254ac6d5eae.png)\n\n**Day205 of 300DaysOfData!**\n- **SGD and Minibatches**: The process to change or update the weights based on the gradients in order to consider some of the details involved in the next phase of the learning process is called an Optimization Step. The calculation of average loss for a few data items at a time is called a Minibatch. The number of data items in the Minibatch is called Batchsize. A larger Batchsize means more accurate and stable estimate of the dataset gradients from the loss function whereas a single Batchsize result in an imprecise and unstable gradient. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Calculating Gradients and Back Propagation, Weights, Bias and Parameters, Zeroing Gradients, Training Loop and Learning Rate, Accuracy and Evaluation, Creating an Optimizer and few more topics related to the same from here. I have presented the implementation of Calculating Gradients, Accuracy and Training using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5b8a90871b50.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2dd9b98b8b35.png)\n\n**Day206 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Creating an Optimizer, Linear Module, Weights and Biases, Model Parameters, Optimization and Zeroing Gradients, SGD Class, Data Loaders and Learner Class of Fastai and few more topics related to the same from here. I have presented the implementation of Creating Optimizer and Learner Class using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3204337a034f.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4122733442ae.png)\n\n**Day207 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Adding a Nonlinearity, Simple Linear Classifiers, Basic Neural Networks, Weight and Bias Tensors, Rectified Linear Unit or RELU Activation Function, Universal Approximation Theorem, Sequential Module and few more topics related to the same from here. I have presented the implementation of Creating Simple Neural Networks using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Training Classifier**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_509a0894cc69.png)\n\n**Day208 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Image Classification, Localization, Regular Expressions, Data Block and Data Loaders, Regex Labeller, Data Augmentation, Presizing, Checking and Debugging Data Block, Item and Batch Transformations and few more topics related to the same from here. I have presented the implementation of Creating and Debugging Data Block and Data Loaders using Fastai and PyTorch here in the snapshot. I have used Resize as an item transform with a large size and RandomResizedCrop as a batch transform with a smaller size. RandomResizedCrop will be added if min scale parameter is passed in aug transforms function as was done in DataBlock call below. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_acace20ad7b8.png)\n\n**Day209 of 300DaysOfData!**\n- **Exponential Function**: Exponential Function is defined as e\\*\\*x where e is a special number approximately equal to 2.718. It is the inverse of natural logarithm function. Exponential Function is always positive and increases very rapidly. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Cross Entropy Loss Function, Viewing Activations and Labels, Softmax Activation Function, Sigmoid Function, Exponential Function, Negative Log Likelihood, Binary Classification and few more topics related to the same from here. I have presented the implementation of Softmax Function and Negative Log Likelihood using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_42822e5dc8a6.png)\n\n**Day210 of 300DaysOfData!**\n- **Exponential Function**: Exponential Function is defined as e\\*\\*x where e is a special number approximately equal to 2.718. It is the inverse of natural logarithm function. Exponential Function is always positive and increases very rapidly. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Logarithmic Function, Negative Log Likelihood, Cross Entropy Loss Function, Softmax Function, Model Interpretation, Confusion Matrix, Improving the Model, The Learning Rate Finder, Logarithmic Scale and few more topics related to the same from here. I have presented the implementation of Cross Entropy Loss, Confusion Matrix and Learning Rate Finder using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e18f4b7c5a0e.png)\n\n**Day211 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Unfreezing and Transfer Learning, Freezing Trained Layers, Discriminative Learning Rates, Selecting the Number of Epochs, Deeper Architectures and few more topics related to the same from here. I have presented the implementation of Unfreezing and Transfer Learning and Discriminative Learning Rates using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Image Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_40e2cb2cc6ea.png)\n\n**Day212 of 300DaysOfData!**\n- **Multilabel Classification**: Multilabel Classification refers to the problem of identifying the categories of objects in images that may not contain exactly one type of object. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Questionnaire of Image Classification, Multilabel Classification and Regression, Pascal Dataset, Pandas and DataFrames, Constructing DataBlock, Datasets and DataLoaders, Lambda Functions and few more topics related to the same from here. I have presented the implementation of Creating DataBlock and DataLoaders using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Multilabel Classification & Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2aaecf1df3d4.png)\n\n**Day213 of 300DaysOfData!**\n- **Multilabel Classification**: Multilabel Classification refers to the problem of identifying the categories of objects in images that may not contain exactly one type of object. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Lambda Functions, Transformation Blocks such as Image Block and Multi Category Block, One Hot Encoding, Data Splitting, DataLoaders, Datasets and DataBlock, Resizing and Cropping and few more topics related to the same from here. I have presented the implementation of Creating DataBlock and DataLoaders using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Multilabel Classification & Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bebab06b9d66.png)\n\n**Day214 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Binary Cross Entropy Loss Function, DataLoaders and Learner, Getting Model Activations, Sigmoid and Softmax Functions, One Hot Encoding, Getting Accuracy, Partial Function and few more topics related to the same from here. **F.binary_cross_entropy** and its module equivalent **nn.BCELoss** calculate cross entropy on a one hot encoded target but don't include the initial sigmoid. Normally, **F.binary_cross_entropy_with_logits** or **nn.BCEWithLogitsLoss** do both sigmoid and binary cross entropy in a single function. Similarly for single label dataset, **F.nll_loss** or **nn.NLLoss** for the version without initial softmax and F.cross_entropy or nn.CrossEntropyLoss for the version with initial softmax. I have presented the implementation of Cross Entropy Loss Functions and Accuracy using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Multilabel Classification & Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_84076ba68fe8.png)\n\n**Day215 of 300DaysOfData!**\n-  On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Multilabel Classification and Threshold, Sigmoid Activation, Overfitting, Image Regression, Validation Loss and Metrics, Partial Function and few more topics related to the same from here. **F.binary_cross_entropy** and its module equivalent **nn.BCELoss** calculate cross entropy on a one hot encoded target but don't include the initial sigmoid. Normally, **F.binary_cross_entropy_with_logits** or **nn.BCEWithLogitsLoss** do both sigmoid and binary cross entropy in a single function. Similarly for single label dataset, **F.nll_loss** or **nn.NLLoss** for the version without initial softmax and F.cross_entropy or nn.CrossEntropyLoss for the version with initial softmax. I have presented the implementation of Training the Convolutions with Accuracy and Threshold using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Multilabel Classification & Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n  - [**Fastai: Image Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FRegression.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_011d851b4a1a.png)\n\n**Day216 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Image Regression and Localization, Assembling the Dataset, Initializing DataBlock and DataLoaders, Points and Data Augmentation, Training the Model, Sigmoid Range, MSE Loss Function, Transfer Learning and few more topics related to the same from here. I have presented the implementation of Initializing DataBlock and DataLoaders and Training Image Regression using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai: Multilabel Classification & Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n  - [**Fastai: Image Regression**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FRegression.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9f337e9727a4.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b0b3e7558a72.png)\n\n**Day217 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Imagenette Classification, DataBlock and DataLoaders, Data Normalization and Normalize Function, Progressive Resizing and Data Augmentation, Transfer Learning, Mean and Standard Deviation and few more topics related to the same from here. **Progressive Resizing** is the process of gradually using larger and larger images as training progresses. I have presented the implementation of Initializing DataBlock and DataLoaders, Normalization and Progressive Resizing using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Advanced Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F6.%20Advanced%20Classification\u002FImagenetteClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9152fe19416b.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3fc18a6274f3.png)\n\n**Day218 of 300DaysOfData!**\n- **Label Smoothing**: Label Smoothing is a process which replaces all the labels i.e 1s with a number a bit less than 1 and 0s with a number a bit more than 0 for training. It will make training more robust even if there is mislabeled data which results to be a model that generalizes better at inference. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Progressive Resizing, Test Time Augmentation, Mixup Augmentation, Linear Combinations, Callbacks, Label Smoothing and Cross Entropy Loss Function and few more topics related to the same from here. During inference or validation, creating multiple versions of each image using data augmentation and then taking the average or maximum of the predictions for each augmented version of the image is called **Test Time Augmentation**. I have presented the implementation of Progressive Resizing, Test Time Augmentation, Mixup Augmentation and Label Smoothing using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Advanced Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F6.%20Advanced%20Classification\u002FImagenetteClassification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2cd593b58433.png)\n\n**Day219 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Collaborative Filtering, Learning the Latent Factors, Loss Function and Stochastic Gradient Descent, Creating DataLoaders, Batches, Dot Product and Matrix Multiplication and few more topics related to the same from here. The mathematical operation of multiplying the elements of two vectors together and then summing up the result is called **Dot Product**. I have presented the implementation of Initializing Dataset and Creating DataLoaders using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Collaborative Filtering**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9bbcb0faf630.png)\n\n**Day220 of 300DaysOfData!**\n- **Embedding**: The special layer that indexes into a vector using an integer but has its derivative calculated in such a way that it is identical to what it would have been if it had done a matrix multiplication with a one hot encoded vector is called **Embedding**. Multiplying by a one hot encoded matrix using the computational shortcut that it can be implemented by simply indexing directly. The thing that multiply the one hot encoded matrix is called the **Embedding Matrix**. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Creating DataLoaders, Embedding Matrix, Collaborative Filtering, Object Oriented Programming with Python, Inheritance, Module and Forward Propagation Function, Batches and Learner, Sigmoid Range and few more topics related to the same from here. I have presented the implementation Embedding, Dot Product Class and Sigmoid Range using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Collaborative Filtering**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_94eec8b48259.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_686065918219.png)\n\n**Day221 of 300DaysOfData!**\n- **Embedding**: The special layer that indexes into a vector using an integer but has its derivative calculated in such a way that it is identical to what it would have been if it had done a matrix multiplication with a one hot encoded vector is called **Embedding**. Multiplying by a one hot encoded matrix using the computational shortcut that it can be implemented by simply indexing directly. The thing that multiply the one hot encoded matrix is called the **Embedding Matrix**. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Collaborative Filtering, Weight Decay or L2 Regularization, Overfitting, Creating Embeddings and Weight Matrices, Parameter Module and few more topics related to the same from here. **Weight Decay** consists of adding sum of the squared weights to the loss function. The idea is that the larger the coefficients are, the sharper the canyons will be in the loss function. I have presented the implementation of Biases and Weight Decay and Matrices using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Collaborative Filtering**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2373a9587857.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_19a1e3dbc4dc.png)\n\n**Day222 of 300DaysOfData!**\n- **Embedding**: The special layer that indexes into a vector using an integer but has its derivative calculated in such a way that it is identical to what it would have been if it had done a matrix multiplication with a one hot encoded vector is called **Embedding**. Multiplying by a one hot encoded matrix using the computational shortcut that it can be implemented by simply indexing directly. The thing that multiply the one hot encoded matrix is called the **Embedding Matrix**. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Interpreting Embedding and Biases, Principal Component Analysis or PCA, Collab Learner, Embedding Distance and Cosine Similarity, Bootstrapping a Collaborative Filtering Model, Probabilistic Matrix Factorization or Dot Product Model and few more topics related to the same from here. I have presented the implementation Interpreting Biases, Collab Learner Model and Embedding Distance using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Collaborative Filtering**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7f6a6d44c7ca.png)\n\n**Day223 of 300DaysOfData!**\n-  On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Deep Learning and Collaborative Filtering, Embedding Matrices, Linear Function, RELU and Nonlinear Functions, Sigmoid Range, Forward Propagation Function, Tabular Model and Embedding Neural Networks and few more topics related to the same from here. In Python kwargs in a parameter list means \"put any additional keyword arguments into a dict called kwargs.\" And kwargs in an argument list means \"insert all key and value pairs in the kwargs dict as named arguments here.\" I have presented the implementation Deep Learning for Collaborative Filtering and Neural Networks using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Collaborative Filtering**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3793de974678.png)\n\n**Day224 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Tabular Modeling, Categorical Embeddings, Continuous and Categorical Variables, Recommendation System, The Tabular Dataset, Ordinal Columns, Decision Trees, Handling Dates, Tabular Pandas and Tabular Proc Object and few more topics related to the same from here. I have presented the implementation of Handling Dates, Tabular Pandas and Tabular Proc using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6fc0fc03c2f0.png)\n\n**Day225 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Tabular Modeling, Creating the Decision Tree, Leaf Nodes, Root Mean Squared Error, DTreeviz Library, Stopping Criterion, Overfitting and few more topics related to the same from here. I have presented the implementation of Creating Decision Tree and Leaf Nodes using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d8d7d6f6c462.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c893c73b5ef4.png)\n\n**Day226 of 300DaysOfData!**\n- **Random Forest**: A Random Forest is a model that averages the predictions of a large number of decision trees which are generated by randomly varying various parameters that specify what data is used to train the tree and other tree parameters. Bagging is a particular approach to ensembling or combining the results of multiple models together. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Categorical Variables, Random Forests and Bagging Predictors, Ensembling, Optimal Parameters, Out of Bag Error, Tree Variance for Prediction Confidence and Standard Deviation, Model Interpretation and few more topics related to the same from here. The **Out of Bag Error** or OOB error is a way of measuring prediction error in the training dataset by including in the calculation of a rows error trees only where that row was not included in the training. I have presented the implementation of Creating Random Forest and Model Interpretation using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_df1968d9d949.png)\n\n**Day227 of 300DaysOfData!**\n- **Random Forest**: A Random Forest is a model that averages the predictions of a large number of decision trees which are generated by randomly varying various parameters that specify what data is used to train the tree and other tree parameters. Bagging is a particular approach to ensembling or combining the results of multiple models together. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Random Forest, Feature Importance, Removing Low Importance Variables, Removing Redundant Features, Determining Similarity of Features, Rank Correlation, OOB Score and few more topics related to the same from here. I have presented the implementation of Random Forest and Feature Importance using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d23f210cbb67.png)\n\n**Day228 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Removing Redundant Features, Determining Similarity, OOB Score, Partial Dependence Plots, Data Leakage, Root Mean Squared Error and few more topics related to the same from here. Standard Deviation of predictions across the trees presents the relative confidence of predictions. The model is more consistent when the Standard Deviation is lower. I have presented the implementation of Removing Redundant Features and Partial Dependence Plots using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !! \n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5d36298e0586.png)\n\n**Day229 of 300DaysofData!**\n- **Random Forest Model** just averages the predictions of a number of trees and therefore it can never predict values outside the range of the training data. **Random Forests** are not able to extrapolate outside the types of data i.e out of domain data. Here prediction is simply the prediction that the Random Forest makes. Here bias is the prediction based on taking the mean of the dependent variable. Similarly contributions tells us the total change in prediction due to each of the independent variables. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Tree Interpreter, Redundant Features, Waterfall Charts or Plots, Random Forest, Prediction, Bias and Contributions, The Extrapolation Problem, Unsqueeze Method, Out of Domain Data and few more topics related to the same from here. I have presented the implementation of Tree Interpreter, Waterfall Plots, Extrapolation Problem using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_64724dbae904.png)\n\n**Day230 of 300DaysOfData!**\n- **Random Forest Model** just averages the predictions of a number of trees and therefore it can never predict values outside the range of the training data. **Random Forests** are not able to extrapolate outside the types of data i.e out of domain data. Here prediction is simply the prediction that the Random Forest makes. Here bias is the prediction based on taking the mean of the dependent variable. Similarly contributions tells us the total change in prediction due to each of the independent variables. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about The Extrapolation Problem and Random Forest, Finding Out of Domain Data, Root Mean Squared Error and Feature Importance, Histograms and few more topics related to the same from here. I have presented the implementation of Finding Out of Domain Data and RMSE using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ed3c266f49d2.png)\n\n**Day231 of 300DaysOfData!**\n- **Random Forest**: Random Forest Model just averages the predictions of a number of trees and therefore it can never predict values outside the range of the training data. Random Forests are not able to extrapolate outside the types of data i.e out of domain data. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Tabular Modeling and Neural Networks, Continuous and Categorical Features, Embedding Matrix, Mean Squared Error and Regression, Tabular Learner and Learning Rate, Ensembling, Bagging and Boosting, Combining Embeddings and few more topics related to the same from here. Ensembling is the generalization technique in which the average of the predictions of several models are used. I have presented the implementation of Tabular Modeling and Neural Networks and Ensembling using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Tabular Modeling**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9fa9bc330bba.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cbcfd3c5cc85.png)\n\n**Day232 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about NLP and Language Model, Self Supervised Learning, Text Preprocessing, Tokenization, Numericalization and Embedding Matrix, Subword and Characters, Tokens and few more topics related to the same from here. Token is a element of a list created by the Tokenization process which could be a word, a part of a word or subword or a single character. I have presented the implementation of Loading the Data and Word Tokenization using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Natural Language Processing**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_831ca4b3e4b1.png)\n\n**Day233 of 300DaysOfData!**\n- **Tokenization**: **Subword Tokenization** splits words into smaller parts based on the most commonly occurring sub strings. **Word Tokenization** splits a sentence on spaces as well as applying language specific rules to try to separate parts of meaning even when there are no spaces. **Subword Tokenization** provides a way to easily scale between character tokenization i.e. using a small subword vocab and word tokenization i.e using a large subword vocab and handles every human language without needing language specific algorithms to be developed. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Word Tokenization, Subword Tokenization, Setup Method, Vocabulary, Numericalization with Fastai, Embedding Matrices and few more topics related to the same from here. I have presented the implementation of Subword Tokenization and Numericalization using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Natural Language Processing**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_33e73d46cbd5.png)\n\n**Day234 of 300DaysOfData!**\n- **Tokenization**: **Subword Tokenization** splits words into smaller parts based on the most commonly occurring sub strings. **Word Tokenization** splits a sentence on spaces as well as applying language specific rules to try to separate parts of meaning even when there are no spaces. **Subword Tokenization** provides a way to easily scale between character tokenization i.e. using a small subword vocab and word tokenization i.e using a large subword vocab and handles every human language without needing language specific algorithms to be developed. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Numericalization with Fastai, Embedding Matrices, Creating Batches for Language Model, Tokenization, Training a Text Classifier, Language Model using DataBlock, Data Loaders, Fine Tuning Language Model and Transfer Learning and few more topics related to the same from here. I have presented the implementation of Creating Data Loaders and Data Block for Language Model using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Natural Language Processing**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e13597531fcb.png)\n\n**Day235 of 300DaysOfData!**\n- **Encoder**: Encoder is defined as the model which doesn't contain task specific final layers. The term Encoder means much the same thing as body when applied to vision CNN but Encoder tends to be more used for NLP and generative models. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Encoder Model, Text Generation and Classification, Creating the Classifier Data Loaders, Embeddings, Data Augmentation, Fine Tuning the Classifier, Discriminative Learning Rates and Gradual Unfreezing, Disinformation and Language Models and few more topics related to the same from here. I have presented the implementation of Training Text Classifier Model using Discriminative Learning Rates and Gradual Unfreezing using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Natural Language Processing**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f290d62dccff.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5fea1444b137.png)\n\n**Day236 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Data Munging with Fastai, Tokenization and Numericalization, Creating Data Loaders and Data Block, Mid Level API, Transforms, Decode Method, Data Augmentation, Cropping and Padding and few more topics related to the same from here. I have presented the implementation of Creating Data Loaders, Tokenization and Numericalization using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Data Munging**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dde5bdbf7153.png)\n\n**Day237 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Data Munging, Decorator, Pipeline Method, Transformed Collections, Training and Validation Set, Data Loaders Object, Categorize Method, Transformations and few more topics related to the same from here. I have presented the implementation of Pipeline Class and Transformed Collections using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Data Munging**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fc28d9c310a8.png)\n\n**Day238 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I I have read about Datasets Class, Transformed Collections, Pipelines, Categorize Method, Data Loaders and Data Block, Text Block, Partial Function, Category Block and few more topics related to the same from here. I have presented the implementation of Datasets Class, Transformed Collections and Data Loaders using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Data Munging**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_454a705b7cc8.png)\n\n**Day239 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Applying Mid Level Data API for Siamese Pair and Computer Vision, Data Loaders, Transforms and Resizing Images, Data Augmentation, Subclasses, Transformed Collections and few more topics related to the same from here. Datasets class will apply two or more pipelines in parallel to the same raw object and build a tuple with the result. It will automatically do the setup and index into a Datasets. I have presented the implementation of Siamese Image Object and Data Augmentation using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Data Munging**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5e5c4b28722f.png)\n\n**Day240 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Siamese Transform Object, Random Splitting, Transformed Collections and Datasets Class, Data Loaders, ToTensor and IntToFloatTensor Methods, Data and Batch Normalization and few more topics related to the same from here. ToTensor method converts images to tensors. IntToFloatTensor method converts the tensor of images containing the integers from 0 to 255 to a tensor of floats and divide by 255 to make values between 0 and 1. I have presented the implementation of Siamese Transform Object and Data Augmentation using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Data Munging**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6d6361260f51.png)\n\n**Day241 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Language Model from Scratch, Data Concatenation and Tokenization, Vocabulary and Numericalization, Neural Networks, Independent Variables and Dependent Variable, Sequence of Tensors and few more topics related to the same from here. I have presented the implementation of Preparing Sequence of Tensors for Language Model using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8ad202ee6a65.png)\n\n**Day242 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Language Model from Scratch using PyTorch, Sequence Tensors, Creating Data Loaders and Batchsize, Neural Network Architecture and Linear Layers, Words Embeddings and Activations, Weight Matrix, Creating Learner and Training and few more topics related to the same from here. I will create neural network architecture that takes three words as input and returns the predictions of the probability of each possible next word in the vocab. I will use three standard linear layers. The first linear layer will use only the first words embedding as activations. The second layer will use the second words embedding plus the first layers output activations and the third layer will use the third words embedding plus the second layers output activations. The key effect is that every word is interpreted in the information context of any words preceding it. Each of these three layers will use the same weight matrix. I have presented the implementation of Creating Data Loaders, Language Model from Scratch and Training using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2eb457924c4c.png)\n\n**Day243 of 300DaysOfData!**\n- **Backpropagation Through Time**: Backpropagation through Time is a process of treating a neural network with effectively one layer per time step as one big model and calculating gradients on it in the usual way. The BPTT technique is used to avoid running out of memory and time which detaches the history of computation steps in the hidden state every few time steps. Hidden State is defined as the activations that are updated at each step of a recurrent neural network. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Recurrent Neural Networks, Hidden State of NN, Improving the RNN, Maintaining the State of RNN, Unrolled Representation, Backpropagation and Derivatives, Detach Method, Stateful RNN, Backpropagation Through Time and few more topics related to the same from here. I have presented the implementation of Recurrent Neural Networks and Language Model using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7020d0060ef7.png)\n\n**Day244 of 300DaysOfData!**\n- **Backpropagation Through Time**: Backpropagation through Time is a process of treating a neural network with effectively one layer per time step as one big model and calculating gradients on it in the usual way. The BPTT technique is used to avoid running out of memory and time which detaches the history of computation steps in the hidden state every few time steps. Hidden State is defined as the activations that are updated at each step of a recurrent neural network. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Backpropagation Through Time, LMDataLoader Object and Arranging the Dataset, Creating Data Loaders, Callbacks and Reset Method, Creating More Signal and few more topics related to the same from here. I have presented the implementation of Arranging Dataset, Creating Data Loaders, Callbacks and Reset Method using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4bc80f743784.png)\n\n**Day245 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Creating More Signal or Sequence, Cross Entropy Loss Function and Flatten Method, Multilayer Recurrent Neural Networks and Activations, Unrolled Representation, Stack and few more topics related to the same from here. The single layer Recurrent Neural Network performed better than Multilayer Recurrent Neural Network because a deeper model leads to exploding and vanishing activations. I have presented the implementation Creating more Signal and Multilayer Recurrent Neural Network using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7c5c8a29038e.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b72e6a4f923c.png)\n\n**Day246 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Exploding and Disappearing Activations, Matrix Multiplication, Architecture of Long Short Term Memory and RNN, Sigmoid and Tanh Function, Hidden State and Cell State, Forget Gate, Input Gate, Cell Gate and Output Gate, Chunk Method and few more topics related to the same from here. I have presented the implementation Long Short Term Memory using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_354e95bca695.png)\n\n**Day247 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Training Language Model using LSTM, Embedding Layer, Linear Layer, Overfitting and Regularization of LSTM, Dropout Regularization, Training or Inference, Bernoulli Method and few more topics related to the same from here. **Dropout** is a regularization technique which randomly changes some activations to zero at a training time. I have presented the implementation Language Model using Long Short Term Memory and Dropout using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e3902c717bbd.png)\n\n**Day248 of 300DaysOfData!**\n- **Activation Regularization**: Activation Regularization is a process of adding the small penalty to the final activations produced by the LSTM to make it as small as possible. It is a regularization method very similar to weight decay. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Activation Regularization and Temporal Activation Regularization, Language Model using Long Short Term Memory, Weight Decay, Training a Weight Tied Regularized LSTM, Weight Tying and Input Embeddings, Text Learner, Cross Entropy Loss Function and few more topics related to the same from here. I have presented the implementation Language Model using Regularized Long Short Term Memory and Regularized Dropout and Activation Regularization using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Language Model from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dcefe63cd11e.png)\n\n**Day249 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Convolutional Neural Networks, The Magic of Convolutions, Feature Engineering, Kernel and Matrix, Mapping a Convolutional Kernel, Nested List Comprehensions, Matrix Multiplications and few more topics related to the same from here. Feature Engineering is the process of creating a new transformations of the input data in order to make it easier to model. I have presented the implementation of Feature Engineering and Mapping a Convolutional Kernel using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Convolutional Neural Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_31ad8c0d0683.png)\n\n**Day250 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Convolutions with PyTorch, Rank Tensors, Creating Data Block and Data Loaders, Channel of Images, Unsqueeze Method and Unit Axis, Strides and Padding, Understanding the Convolutions Equations, Matrix Multiplication, Shared Weights and few more topics related to the same from here. A channel is a single basic color in an image. For a regular full color images, there are three channels : red, green and blue. Kernels passed to convolutions need to be rank 4 tensors. I have presented the implementation of Convolutions and DataLoaders using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Convolutional Neural Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_104c545c77e5.png)\n\n**Day251 of 300DaysOfData!**\n- **Channels and Features**: Channels and Features are largely used interchangeably and refer to the size of the second axis of a weight matrix which is the number of activations per grid cell after a convolution. Channels refer to the input data i.e colors or activations inside the network. Using a stride 2 convolution often increases the number of Features at the same time because the number of activations in the activation map decrease by the factor of 4. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Convolutional Neural Network, Refactoring, Channels and Features, Understanding Convolution Arithmetic, Biases, Receptive Fields, Convolution over RGB Image, Stochastic Gradient Descent and few more topics related to the same from here. I have presented the implementation of Convolutional Neural Network and Training the Learner using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Convolutional Neural Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1b28badf73b8.png)\n\n**Day252 of 300DaysOfData!**\n- **Channels and Features**: Channels and Features are largely used interchangeably and refer to the size of the second axis of a weight matrix which is the number of activations per grid cell after a convolution. Channels refer to the input data i.e colors or activations inside the network. Using a stride 2 convolution often increases the number of Features at the same time because the number of activations in the activation map decrease by the factor of 4. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Improving Training Stability of Convolutional Neural Networks, Batch Size and Splitting the Dataset, Simple Baseline Network, Activations and Kernel Size, Activation Stat Callbacks, Learning Rate, Creating a Learner and Training and few more topics related to the same from here. I have presented the implementation of Convolutional Neural Network and Training the Learner using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Convolutional Neural Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_37b0ce4a19ee.png)\n\n**Day253 of 300DaysOfData!**\n- **One Cycle Training**: 1 Cycle Training is a combination of warmup and annealing. Warmup is the one where learning rate grows from the minimum value to the maximum value and Annealing is the one where it decreases back to the minimum value. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Activation Stats Callbacks, Increasing Batch Size, Activations, 1 Cycle Training, Warmup and Annealing, Super Convergence, Learning Rate and Momentum, Colorful Dimension and Histograms and few more topics related to the same from here. I have presented the implementation of Increasing Batch Size, 1 Cycle Training and Inspecting Momentum and Activations using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Convolutional Neural Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_09c565db633d.png)\n\n**Day254 of 300DaysOfData!**\n- **Fully Convolutional Networks**: The idea in Fully Convolutional Networks is to take the average of activations across a convolutional grid. A Fully Convolutional Networks has a number of convolutional layers, some of which will be stride 2 convolutions at the end of which is an adaptive average pooling layer, a flatten layer to remove the unit axis and finally a linear layer. Larger batches have gradients that are more accurate since they are calculated from more data. But larger batch size means fewer batches per epoch which means fewer opportunities for the model to update weights. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Residual Networks or ResNets, Convolutional Neural Networks, Strides and Padding, Fully Convolutional Networks, Adaptive Average Pooling Layer, Flatten Layer, Activations and Matrix Multiplications and few more topics related to the same from here. I have presented the implementation of Preparing Data and Fully Convolutional Networks using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Residual Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bef8be5ae7ef.png)\n\n**Day255 of 300DaysOfData!**\n- **Fully Convolutional Networks**: The idea in Fully Convolutional Networks is to take the average of activations across a convolutional grid. A Fully Convolutional Networks has a number of convolutional layers, some of which will be stride 2 convolutions at the end of which is an adaptive average pooling layer, a flatten layer to remove the unit axis and finally a linear layer. Larger batches have gradients that are more accurate since they are calculated from more data. But larger batch size means fewer batches per epoch which means fewer opportunities for the model to update weights. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Fully Convolutional Neural Networks, Building ResNet, Skip Connections, Identity Mapping, SGD, Batch Normalization Layer, Trainable Parameters, True Identity Path, Convolutional Neural Networks, Average Pooling Layer and few more topics related to the same from here. I have presented the implementation of ResNet Architecture and Skip Connections using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Residual Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9cd077374b94.png)\n\n**Day256 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Residual Networks, RELU Activation Function, Skip Connections, Training Deeper Models, Loss Landscape of NN, Stem of the Network, Convolutional Layers, Max Pooling Layer and few more topics related to the same from here. Stem is defined as the first few layers of CNN. It has different structure than the main body of CNN. I have presented the implementation of Training Deeper Models and Stem of Network using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Residual Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_58f378409f11.png)\n\n**Day257 of 300DaysOfData!**\n- **Bottleneck Layers**: Bottleneck Layers use three convolutions: Two 1X1 at the begining and the end and one 3X3. The 1X1 convolutions are much faster which facilitates to use higher number of filters in and out. The 1X1 convolutions diminish and then restore the number of channels so called Bottleneck. The overall impact is to facilitate the use of more filters in the same amount of time. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Stem of the Network, Residual Network Architecture, Bottleneck Layers, Convolutional Neural Networks, Progressive Resizing and few more topics related to the same from here. I have presented the implementation of Training Deeper Networks and Bottleneck Layers using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Residual Networks**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_47403290a305.png)\n\n**Day258 of 300DaysOfData!**\n- **Splitter Function**: A splitter is a function that tells the fastai library how to split the model into parameter groups which are used to train only the head of the model during transfer learning. The params is just a function that returns all parameters of a given module. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Body and Head of Networks, Batch Normalization Layer, Unet Learner and Architecture, Generative Vision Models, Nearest Neighbor Interpolation, Transposed Convolutions, Siamese Network, Loss Function and Splitter Function and few more topics related to the same from here. I have presented the implementation of Siamese Network Model, Loss Function and Splitter Function using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Architecture Details**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F14.%20Architecture%20Details\u002FArchitectures.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_634922ed5cb8.png)\n\n**Day259 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Stochastic Gradient Descent, Loss Function, Updating Weights, Optimization Function, Creating Data Block and Data Loaders, ResNet Model and Learner, Training Process and few more topics related to the same from here. I have presented the implementation of Preparing Dataset and Baseline Model using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Training Process**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7fa5a0d8854e.png)\n\n**Day260 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Training Process, Stochastic Gradient Descent, Optimization Function, Learning Rate Finder, Momentum, Optimizer Callbacks, Zeroing Gradients, Partial Function and few more topics related to the same from here. I have presented the implementation of Functions for Optimizer and SGD here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Training Process**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_94a317fdd4c7.png)\n\n**Day261 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Stochastic Gradient Descent and Optimization Function, Momentum, Exponentially Weighted Moving Average, Gradient Averages, Callbacks, RMS Prop, Adaptive Learning Rate, Divergence and Epsilon and few more topics related to the same from here. I have presented the implementation of Momentum and RMS Prop using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Training Process**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_82c11a201b3f.png)\n\n**Day262 of 300DaysOfData!**\n- **Adam Optimizer**: Adam mixes the idea of SGD with momentum and RMSProp together where it uses the moving average of the gradients as a direction and divides by the square root of the moving average of the gradients squared to give an adaptive learning rate to each parameter. It takes the unbiased moving average. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about RMSProp Optimizer, SGD, Adam Optimizer, Unbiased Moving Average of Gradients, Momentum Parameter, Decoupled Weight Decay, L1 and L2 Regularization, Callbacks and few more topics related to the same from here. I have presented the implementation of RMS Prop and Adam Optimizer using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Training Process**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_588e1b7772b6.png)\n\n**Day263 of 300DaysOfData!**\n- **Adam Optimizer**: Adam mixes the idea of SGD with momentum and RMSProp together where it uses the moving average of the gradients as a direction and divides by the square root of the moving average of the gradients squared to give an adaptive learning rate to each parameter. It takes the unbiased moving average. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Creating Callbacks, Loss Functions, Model Resetter Callbacks, RNN Regularization, Callback Ordering and Exceptions, Stochastic Gradient Descent and few more topics related to the same from here. I have presented the implementation of Model Resetter Callback and RNN Regularization Callback using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Training Process**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8375c2844509.png)\n\n**Day264 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Neural Networks, Building a Neural Network from Scratch, Modeling a Neuron, Nonlinear Activation Functions, Hidden Size, Fully Connected Layer and Dense Layer, Linear Layer, Matrix Multiplication from Scratch, Elementwise Arithmetic and few more topics related to the same from here. I have presented the implementation of Matrix Multiplication from Scratch and Elementwise Arithmetic using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n  \n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ff71ca4fda31.png)\n\n**Day265 of 300DaysOfData!**\n- **Forward and Backward Passes**: Computing all the gradients of a given loss with respect to its parameters is known as Backward Pass. Similarly computing the output of the model on a given input based on the matrix products is known as Forward Pass. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Broadcasting with Scalar, Broadcasting Vector and Matrix, Unsqueeze Method, Einstein Summation, Matrix Multiplication, The Forward and Backward Passes, Defining and Initializing Layer, Activation Function, Linear Layer, Weights and Biases and few more topics related to the same from here. I have presented the implementation of Einstein Summation and Defining and Initializing Linear Layer using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_67051f112dd7.png)\n\n**Day266 of 300DaysOfData!**\n- **Forward and Backward Passes**: Computing all the gradients of a given loss with respect to its parameters is known as Backward Pass. Similarly computing the output of the model on a given input based on the matrix products is known as Forward Pass. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Mean and Standard Deviation, Matrix Multiplications, Xavier Initialization, RELU Activation, Kaiming Initialization, Weights and Activations and few more topics related to the same from here. I have presented the implementation of Xavier Initialization, RELU Activation, and Matrix Multiplications using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7229ca6219fd.png)\n\n**Day267 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Kaiming Initialization, Forward Pass, Mean Squared Error Loss Function, Gradients and Backward Pass, Linear Layers and RELU Activation Function, Chain Rule, Backpropagation and few more topics related to the same from here. I have presented the implementation of Kaiming Initialization, MSE Loss Function and Gradients using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b040f828be3f.png)\n\n**Day268 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Gradients of Matrix Multiplication, Symbolic Computation, Forward and Backward Propagation Function, Model Parameters, Weights and Biases, Refactoring the Model, Callable Module and few more topics related to the same from here. I have presented the implementation of RELU Module, Linear Module and Mean Squared Error Module using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0c598302629f.png)\n\n**Day269 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Initializing Model Architecture, Callable Function, Forward and Backward Propagation Function, Linear Function, Mean Squared Error Loss Function, RELU Activation Function, Back Propagation Function and Gradients, Squeeze Function and few more topics related to the same from here. I have also read about Perturbations and Neural Networks, Vanishing Gradients and Convolutional Neural Networks.  I have presented the implementation of Defining Model Architecture, Layer Function and RELU using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_629b0347bb0d.png)\n\n**Day270 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Defining Base Class and Sub Classes, Linear Layer, RELU Activation Function and Non Linearities, Mean Squared Error Function, Super Class Initializer, Kaiming Initialization, Elementwise Arithmetic and Broadcasting and few more topics related to the same from here. I have presented the implementation of Defining Linear Layer and Linear Model using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Neural Network Foundations**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9aea492b6620.png)\n\n**Day271 of 300DaysOfData!**\n- **Class Activation Map**: The Class Activation Map uses the output of the last convolutional layer which is just before the average pooling layer together with predictions to give a heatmap visualization of model decision. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about CNN Interpretation, Class Activation Map, Hooks, Heatmap Visualization, Activations and Convolutional Layer, Dot Product, Feature Map, Data Loaders and few more topics related to the same from here. I have presented the implementation of Defining Hook Function and Decoding Images using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**CNN Interpretation with CAM**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F17.%20CNN%20Interpretation\u002FCNN%20Interpretation.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_21b7a54e88e0.png)\n\n**Day272 of 300DaysOfData!**\n- **Class Activation Map**: The Class Activation Map uses the output of the last convolutional layer which is just before the average pooling layer together with predictions to give a heatmap visualization of model decision. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Hook Class and Context Manager, Gradient Class Activation Map, Heatmap Visualization, Activations and Weights, Gradients and Back Propagation, Model Interpretation and few more topics related to the same from here. I have presented the implementation of Defining Hook Function, Activations, Gradients and Heatmap Visualization using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**CNN Interpretation with CAM**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F17.%20CNN%20Interpretation\u002FCNN%20Interpretation.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_52c585b7f095.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_89fa67898db1.png)\n\n**Day273 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Fastai Learner from Scratch, Dependent and Independent Variable, Vocabulary, Dataset and Indexing and few more topics related to the same. I have also read about Convolutional Neural Networks, Perturbations and Loss Functions. I have presented the implementation of Preparing Training and Validation Dataset using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai Learner from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e44c2a2d6fb4.png)\n\n**Day274 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Creating Collation Function, Parallel Preprocessing, Decoding Images, Data Loader Class, Normalization and Image Statistics, Permuting Axis Order, Precision and few more topics related to the same from here. I have presented the implementation of Initializing Data Loader and Normalization using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai Learner from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d8c6ca7d068e.png)\n\n**Day275 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Module and Parameter, Forward Propagation Function, Convolutional Layer, Training Attributes, Kaiming Normalization and Xavier Normalization Initializer, Transformation Function, Weights and Biases, Linear Model, Tensors and few more topics related to the same from here. I have presented the implementation of Defining Module : Convolutional Layer and Linear Model using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai Learner from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_17784d8ad43f.png)\n\n**Day276 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Convolutional Neural Networks, Linear Model, Testing Module, Sequential Module, Parameters, Adaptive Pooling Layer and Mean, Stride, Hook Function, Pipeline and few more topics related to the same from here. I have presented the implementation of Testing Module, Sequential Module and Convolutional Neural Network using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai Learner from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98be4032e100.png)\n\n**Day277 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Loss Function, Negative Log Likelihood Function, Log Softmax Function, Log of Sum of Exponentials, Stochastic Gradient Descent Optimizer Function, Data Loaders, Training and Validation Sets and few more topics related to the same from here. I have presented the implementation of Negative Log Likelihood Function, Cross Entropy Loss Function, SGD Optimizer and Data Loaders using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai Learner from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b523a3415add.png)\n\n**Day278 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Data, Convolutional Neural Net Model, Loss Function, Stochastic Gradient Descent and Optimization Function, Learner, Callbacks, Parameters, Training and Epochs and few more topics related to the same from here. I have presented the implementation of Learner and Callbacks using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Fastai Learner from Scratch**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_df947f01fade.png)\n\n**Day279 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read Binary Classification, Chest X-Rays, DICOM or Digital Imaging and Communications in Medicine, Plotting the DICOM Data, Random Splitter Function, Medical Imaging, Pixel Data and few more topics related to the same from here. I have presented the implementation of Getting DICOM Files and Inspection using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Chest X-Rays Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1d770d10214c.png)\n\n**Day280 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Binary Classification, Initializing Data Block and Data Loaders, Image Block and Category Block, Batch Transformations, Training Pretrained Model, Learning Rate Finder, Tensors and Probabilities, Model Interpretation and few more topics related to the same from here. I have presented the implementation of Initializing Data Block and Data Loaders, Training Pretrained Model and Interpretation using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Chest X-Rays Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4f034a80f559.png)\n\n**Day281 of 300DaysOfData!**\n- **Sensitivity & Specificity**: Sensitivity = True Positive \u002F (True Positive + False Negative). It is also known as a Type II Error. Specificity = True Negative \u002F (False Positive + True Negative). It is also known as a Type I Error. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Sensitivity and Specificity, Positive Predictive Value and Negative Predictive Value, Confusion Matrix and Model Interpretation, Type I & II Error, Accuracy and Prevalence and few more topics related to the same from here. I have presented the implementation of Confusion Matrix, Sensitivity and Specificity, Accuracy using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Chest X-Rays Classification**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a3194c82f68a.png)\n\n**Day282 of 300DaysOfData!**\n- **Cross Validation**: Cross Validation is a step in the process of building a machine learning model which helps us to ensure that our models fit the data accurately and also ensures that we do not overfit. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about Supervised and Unsupervised Learning, Features, Samples and Targets, Classification and Regression, Clustering, T-Distributed Stochastic Neighbour Embedding, 2D Arrays, Cross Validation, Overfitting and few more topics related to the same from here. I have presented the implementation of TSNE Decomposition and Preparing Dataset here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Supervised and Unsupervised Learning**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d17cefc9f311.png)\n\n**Day283 of 300DaysOfData!**\n- **Cross Validation**: Cross Validation is a step in the process of building a machine learning model which helps us to ensure that our models fit the data accurately and also ensures that we do not overfit. On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about Decision Trees and Classification, Features and Parameters, Accuracy and Model Predictions, Overfitting and Model Generalization, Training Loss and Validation Loss, Cross Validation and few more topics related to the same from here. I have presented the implementation of Decision Tree Classifier and Model Evaluation here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Supervised and Unsupervised Learning**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fe41dc14f6d1.png)\n\n**Day284 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about Stratified KFold Cross Validation, Skewed Dataset and Classification, Data Distribution, Hold Out Cross Validation, Time Series Data, Regression and Sturge's Rule, Probabilities, Evaluation Metrics and Accuracy and few more topics related to the same from here. I have presented the implementation of Distribution of Labels and Stratified KFold here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Supervised and Unsupervised Learning**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_15b97af623a0.png)\n\n**Day285 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about Evaluation Metrics and Accuracy Score, Training and Validation Set, Precision and Recall, True Positive and True Negative, False Positive and False Negative, Binary Classification and few more topics related to the same from here. I have presented the implementation of True Negative, False Negative, False Positive and Accuracy Score here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Evaluation Metrics**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8c8b49cd3cc4.png)\n\n**Day286 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about True Positive Rate, Recall and Sensitivity, False Positive Rate and Specificity, Area Under ROC Curve, Prediction, Probability and Thresholds, Log Loss Function, Multiclass Classification and Macro Averaged Precision and few more topics related to the same from here. I have presented the implementation of True Negative Rate, False Positive Rate, Log Loss Function and Macro Averaged Precision here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Evaluation Metrics**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8a727a6a3779.png)\n\n**Day287 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about Multiclass Classification, Macro Averaged Precision, Micro Averaged Precision, Weighted Precision, Recall Metrics, Random Forest Regressor, Mean Squared Error, Root Mean Squared Error and few more topics related to the same from here. I have presented the implementation of Micro Averaged Precision and Weighted Precision here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Evaluation Metrics**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bf1fa3ebcd81.png)\n\n**Day288 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about Recall Metrics for Multiclass Classification, Weighted F1 Score, Confusion Matrix, Type I Error and Type II Error, AUC Curve, Multilabel Classification and Average Precision and few more topics related to the same from here. I have presented the implementation of Weighted F1 Score and Average Precision here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Evaluation Metrics**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ca73029e358.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_aabe94436e8a.png)\n\n**Day289 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Approaching Almost Any Machine Learning Problem**. Here, I have read about Regression Metrics such as Mean Absolute and Average Error, Root Mean Squared Error, Squared Logarithmic Error, Mean Absolute Percentage Error, R-Squared and Coefficient of Determination, Cohen's Kappa Score, MCC Score and few more topics related to the same from here. I have presented the implementation of Mean Absolute and Average Error, Squared Logarithmic Error, Mean Absolute Percentage Error, R-Squared and MCC Score here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Evaluation Metrics**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3ba52741364b.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_87822b256ae1.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a1fd4e325efc.png)\n\n**Day290 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented about Object Detection and Fine Tuning, Image Segmentation, Tensors and Aspect Ratio, Arrays, Dataset and Data Loaders. I have also started the Machine Learning Engineering for Production Specialization from Coursera. Here, I have read about Steps of ML Project and Case Study, ML Project Lifecycle and few more topics related to the same from here. I have presented the implementation of Dataset Class here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resource: \n  - **Machine Learning Engineering for Production**\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a80af5e3faa7.png)\n\n**Day291 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about OpenCV, Loading and Displaying an Image, Accessing Pixels, Array Slicing and Cropping, Resizing Images, Rotating Image, Smoothing Image, Drawing on an Image and few more topics related to the same. I have also read about ML Project Lifecycle, Deployment Patterns and Pipeline Monitoring from Machine Learning Engineering for Production Specialization of Coursera. I have presented the implementation of OpenCV in Resizing and Rotating and Image, Smoothing and Drawing on an Image here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**OpenCV Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOpenCV.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_82d28dd3b5bf.png)\n\n**Day292 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about OpenCV, Counting Objects, Converting Image to Grayscale, Edge Detection, Thresholding, Detecting and Drawing Contours, Erosions and Dilations, Masking and Bitwise Operations and few more topics related to the same from here. I have also read about Modeling Overview, Key Challenges and Low Average Error from Machine Learning Engineering for Production Specialization of Coursera. I have presented the implementation of OpenCV in Converting Image to Grayscale, Edge Detection, Thresholding, Detecting and Drawing Contours, Erosions and Dilations here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**OpenCV Notebook**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOpenCV.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4f61b6ea52e8.png)\n\n**Day293 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about OpenCV, Rotating Images, Image Preprocessing, Rotation Matrix and Center Coordinates, Image Parsing, Edge Detection and Contour Detection, Masking and Blurring Images and few more topics related to the same from here. I have also read about Baseline Model, Selecting and Training Model, Error Analysis and Prioritization from Machine Learning Engineering for Production Specialization of Coursera. I have presented the implementation of OpenCV in Rotating Images and Getting ROI of Images here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**OpenCV Project I**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20I.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a720268f39cc.png)\n\n**Day294 of 300DaysOfData!**\n- **Histogram Matching**: Histogram Matching can be used as a normalization technique in an image processing pipeline as a form of color correction and color matching which allows to obtain a consistent, normalized representation of images even if lighting conditions change. On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about OpenCV, Color Detection, RGB Colorspace, Histogram Matching, Pixel Distribution, Cumulative Distribution, Resizing Image and few more topics related to the same from here. I have also read about Skewed Datasets, Performance Auditing, Data Centric AI Development and Data Augmentation from Machine Learning Engineering for Production Specialization of Coursera. I have presented the implementation of OpenCV in Histogram Matching here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**OpenCV Project II**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20II.ipynb)\n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_69ee5757b2e7.png)\n\n**Day295 of 300DaysOfData!**\n- **Histogram Matching**: Histogram Matching can be used as a normalization technique in an image processing pipeline as a form of color correction and color matching which allows to obtain a consistent, normalized representation of images even if lighting conditions change. On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about Convolutional Neural Networks, Convolutional Matrix, Kernels, Spatial Dimensions, Padding, ROI of Image, Elementwise Multiplication and Addition, Rescaling Intensity, Laplacian Kernel, Detecting Blur and Smoothing and few more topics related to the same from here. I have presented the implementation of Convolution Method and Constructing Kernels here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**Convolution**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetwork\u002FConvolutions.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c20efe883895.png)\n\n**Day296 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about Convolutional Layers, Filters and Kernel Size, Strides, Padding, Input Data Format, Dilation Rate, Activation Function, Weights and Biases, Kernel and Bias Initializer and Regularizer, Generalization and Overfitting, Kernel and Bias Constraint, Caltech Dataset, Strided Net and few more topics related to the same from here.  I have presented the implementation of Strided Net here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**Convolutional Layer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f504525f28d3.png)\n\n**Day297 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about CNN Architecture, Strided Net, Label Binarizer and One Hot Encoding, Image Data Generator and Data Augmentation, Loading and Resizing Images and few more topics related to the same from here. I have presented the implementation of Label Binarizer and Preparing Dataset here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**Convolutional Layer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_15a5cfb8ebb1.png)\n\n**Day298 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from PyImageSearch Blogs. Here, I have read about Convolutional Neural Networks, Adam Optimization Function, Compiling and Training Strided Net Model, Data Augmentation and Image Data Generator, Classification Report, Plotting Training Loss and Accuracy, Overfitting and Generalization and few more topics related to the same from here. I have presented the implementation of Compiling and Training Model, Classification Report, Training Loss and Accuracy here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Resources: \n  - **Machine Learning Engineering for Production**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**Convolutional Layer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f6a94a9d6873.png)\n\n**Day299 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Transformers Model, GPT2 Pretrained Model and Tokenizer, Encodes and Decodes Methods, Preparing Dataset, Transform Method, Data Loaders and few more topics related to the same from here. I have presented the implementation of Pretrained GPT2 Model and Tokenizer and Transformed DataLoaders using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Transformers**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F20.%20Transformers\u002FTransformers.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7a17499df894.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0580b9ce0eaa.png)\n\n**Day300 of 300DaysOfData!**\n- On my Journey of Machine Learning and Deep Learning, I have read and implemented from the book **Deep Learning for Coders with Fastai and PyTorch**. Here, I have read about Transformers Model, Data Loaders, Batch Size and Sequence Length, Language Model, Fine Tuning GPT2 Model, Callback, Learner, Perplexity and Cross Entropy Loss Function, Learner Rate Finder, Training and Generating Predictions and few more topics related to the same from here. I have presented the implementation of Initializing DataLoaders, Fine Tuning GPT2Model and LR Finder using Fastai and PyTorch here in the snapshot. I hope you will gain some insights and work on the same. I hope you will also spend some time learning the topics from the Book mentioned below. Excited about the days ahead !!\n- Book:\n  - **Deep Learning for Coders with Fastai and PyTorch**\n  - [**Transformers**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F20.%20Transformers\u002FTransformers.ipynb) \n\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a6ff45d24d63.png)\n![Image](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e3bb239dab3a.png)\n","# **300天数据之旅：机器学习与深度学习**\n\n\n![机器学习](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_47648b0a0ffd.jpg)\n\n| 书籍与资源 | 完成状态 |\n| ----- | -----|\n| 1. [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html) | :white_check_mark: |\n| 2. **机器学习全面指南** | :white_check_mark: |\n| 3. **使用Scikit-Learn、Keras和TensorFlow的动手机器学习** | :white_check_mark: |\n| 4. [**语音与语言处理**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F) | |\n| 5. [**机器学习速成课程**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course) | :white_check_mark: |\n| 6. [**用PyTorch进行深度学习：第一部分**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch) | :white_check_mark: |\n| 7. [**深入深度学习**](https:\u002F\u002Fd2l.ai\u002F) | :white_check_mark: |\n| 8. [**逻辑回归文档**](https:\u002F\u002Fml-cheatsheet.readthedocs.io\u002Fen\u002Flatest\u002Flogistic_regression.html) | :white_check_mark: |\n| 9. **使用Fastai和PyTorch的编码者深度学习** | :white_check_mark: |\n| 10. **解决几乎任何机器学习问题的方法** | |\n| 11. [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F) | |\n\n| 研究论文 |\n| --------------- |\n| 1. [**基于梯度的深度架构训练实用建议**](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1206.5533.pdf) |\n\n| 项目与笔记本 |\n| ---------------------- |\n| 1. [**加州房价预测**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git) |\n| 2. [**从零开始实现逻辑回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLogisticRegression\u002FLogisticRegression.ipynb) |\n| 3. [**LeNet网络架构的实现**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb) |\n| 4. [**神经网络风格迁移**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER) |\n| 5. [**基于CIFAR10数据集的图像目标识别**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition) |\n| 6. [**基于ImageNet数据集的犬种分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification) |\n| 7. [**情感分析数据集笔记本**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb) |\n| 8. [**使用RNN进行情感分析**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb) |\n| 9. [**使用CNN进行情感分析**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb) |\n| 10. [**自然语言推理数据集**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb) |\n| 11. [**自然语言推理：注意力机制**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb) |\n| 12. [**自然语言推理：BERT模型**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb) |\n| 13. [**深度卷积生成对抗网络**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb) |\n| 14. [**Fastai：入门笔记本**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb) |\n| 15. [**Fastai：图像检测**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb) |\n| 16. [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb) |\n| 17. [**Fastai：图像分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb) |\n| 18. [**Fastai：多标签分类与回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb) |\n| 19. [**Fastai：图像回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FRegression.ipynb) |\n| 20. [**Fastai：高级分类任务**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F6.%20Advanced%20Classification\u002FImagenetteClassification.ipynb) |\n| 21. [**Fastai：协同过滤**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb) |\n| 22. [**Fastai：表格数据建模**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb) |\n| 23. [**Fastai：自然语言处理**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb) |\n| 24. [**Fastai：数据清洗**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb) |\n| 25. [**Fastai：从零开始构建语言模型**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb) |\n| 26. [**Fastai：卷积神经网络**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb) |\n| 27. [**Fastai：残差网络**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb) |\n| 28. [**Fastai：架构细节**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F14.%20Architecture%20Details\u002FArchitectures.ipynb) |\n| 29. [**Fastai：训练过程**](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7fa5a0d8854e.png) |\n| 30. [**Fastai：神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb) |\n| 31. [**Fastai：利用CAM解释CNN**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F17.%20CNN%20Interpretation\u002FCNN%20Interpretation.ipynb) |\n| 32. [**Fastai：从零开始构建Fastai Learner**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb) |\n| 33. [**Fastai：胸部X光片分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb) |\n| 34. [**监督学习与无监督学习**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb) |\n| 35. [**评估指标**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb) |\n| 36. [**OpenCV笔记本**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOpenCV.ipynb) |\n| 37. [**OpenCV项目I**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20I.ipynb) |\n| 38. [**OpenCV项目II**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20II.ipynb) |\n| 39. [**卷积操作**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetwork\u002FConvolutions.ipynb) |\n| 40. [**卷积层**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb) |\n| 41. [**Fastai：Transformer模型**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F20.%20Transformers\u002FTransformers.ipynb) |\n\n**300天数据之旅第一天！**\n- **梯度下降与交叉验证**：梯度下降是一种迭代方法，用于近似使可微损失函数最小化的参数。交叉验证则是一种重采样技术，用于在有限的数据样本上评估机器学习模型，其核心思想是将数据划分为若干组。在我的机器学习和深度学习之旅中，今天我简要了解了微积分、矩阵、矩阵微积分、随机变量、密度函数、分布、独立性、最大似然估计以及条件概率等基础概念。我还阅读并实现了梯度下降和交叉验证的相关内容。我从零开始这段旅程，并以《从零开始的机器学习》一书为指导。这里展示了梯度下降和交叉验证的具体实现截图。希望你也花些时间阅读上述书籍中的相关内容。我对接下来的日子充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d6613ffd52f6.png)\n\n**300天数据之旅第2天！**\n- **普通线性回归**：线性回归是一种用于建模标量响应变量（因变量）与一个或多个解释变量（自变量）之间关系的线性方法。在我的机器学习和深度学习之旅中，今天我阅读并实现了关于普通线性回归、参数估计、最小化损失和最大化似然等内容，并根据《从零开始的机器学习》一书构建和实现了线性回归模型。此外，我还开始阅读《机器学习全面指南》，这本书侧重于相关主题背后的数学和理论。通过这本书，我学习了回归、普通最小二乘法、向量微积分、正交投影、岭回归、特征工程、拟合椭圆、多项式特征、超参数与验证、误差及交叉验证等内容。我在截图中展示了使用Python实现的线性回归及其可视化效果。希望你也花些时间阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **机器学习全面指南**\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a43d6dbcc656.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f36061adb17c.png)\n\n**300天数据之旅第3天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实现了关于正则化回归（如岭回归和Lasso回归）、贝叶斯回归、广义线性模型以及泊松回归等内容，并根据《从零开始的机器学习》一书进行了相应的构建和实现。同时，我也继续阅读《机器学习全面指南》，该书专注于相关主题背后的数学和理论。通过这本书，我学习了回归中的最大似然估计（MLE）和最大后验估计（MAE）、概率模型、偏差-方差权衡、评估指标、偏差-方差分解、替代分解、多元高斯分布、从数据中估计高斯分布、加权最小二乘法、岭回归和广义最小二乘法等内容。我在截图中展示了使用Python实现的岭回归、Lasso回归、交叉验证、贝叶斯回归和泊松回归。希望你也花些时间阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **机器学习全面指南**\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2bc03f880b51.png)\n\n**300天数据之旅第4天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实现了关于判别分类器的内容，包括二分类和多分类逻辑回归、感知机算法、参数估计、费舍尔线性判别分析及费舍尔准则等，并根据《从零开始的机器学习》一书进行了相应的构建和实现。此外，我还继续阅读《机器学习全面指南》，该书着重讲解相关主题背后的数学和理论。通过这本书，我学习了核函数与岭回归、线性代数推导、计算分析、稀疏最小二乘法、正交匹配追踪、总体最小二乘法、低秩表示、降维、主成分分析、投影、坐标变换、最小化重构误差以及概率主成分分析等内容。我在截图中展示了使用Python实现的二分类和多分类逻辑回归、感知机算法以及费舍尔线性判别分析。希望你也花些时间阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **机器学习全面指南**\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_32b738933069.png)\n\n**300天数据之旅第5天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实现了关于生成式分类器的内容，包括线性判别分析（LDA）、二次判别分析（QDA）、朴素贝叶斯、参数估计及数据似然度等，并根据《从零开始的机器学习》一书进行了相应的构建和实现。同时，我也继续阅读《机器学习全面指南》，该书专注于相关主题背后的数学和理论。通过这本书，我学习了生成式与判别式分类、贝叶斯决策规则、最小二乘支持向量机、特征扩展、神经网络扩展、二分类和多分类逻辑回归、损失函数、训练过程、多分类扩展、高斯判别分析、QDA和LDA分类以及支持向量机等内容。我在截图中展示了使用Python实现的LDA、QDA和朴素贝叶斯，并附带了可视化效果。希望你也花些时间阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **机器学习全面指南**\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2aa78b5d1c1c.png)\n\n**300天数据之旅第6天！**\n- **决策树**：决策树是一种可解释的机器学习方法，适用于回归和分类任务。它是一种类似流程图的结构，其中每个内部节点代表对某个属性的测试，而每个分支则表示测试的结果。在我的机器学习和深度学习之旅中，今天我阅读了关于决策树的内容，包括回归树和分类树、树的构建、分裂与预测、超参数、剪枝与正则化等，并参考《从零开始的机器学习》一书进行了相关理论的学习和实现。此外，我还阅读了《机器学习全面指南》，该书侧重于相关主题背后的数学和理论知识。书中涵盖了决策树学习、熵与信息量、基尼不纯度、停止条件、随机森林、提升算法及AdaBoost、梯度提升以及K均值聚类等内容。我在截图中展示了使用Python实现的回归树和分类树。希望你也花些时间阅读上述主题和书籍。对未来几天充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **机器学习全面指南**\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8bf971cffffb.png)\n\n**300天数据之旅第7天！**\n- **树集成方法**：集成方法通过结合多个简单模型（通常称为“学习器”）的输出，来构建一个方差较低的优秀模型。由于决策树本身具有较高的方差，往往难以达到与其他预测算法相当的精度，而集成方法则可以有效降低这种方差。在我的机器学习和深度学习之旅中，今天我阅读并实现了关于树集成方法的内容，例如用于决策树的Bagging、自助法、随机森林及其操作流程、提升算法、用于二分类的AdaBoost、加权分类树、离散型AdaBoost算法以及用于回归的AdaBoost等，并参考《从零开始的机器学习》一书进行了相关理论的学习和实现。我在截图中展示了使用Python实现的Bagging、随机森林和AdaBoost，并采用了不同的基础估计器。希望你也花些时间阅读上述主题和书籍。对未来几天充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1f72600d1c96.png)\n\n**300天数据之旅第8天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实现了关于神经网络的内容，内容来自《从零开始的机器学习》一书。书中介绍了模型结构、各层之间的通信、激活函数（如ReLU、Sigmoid、线性激活函数）、优化方法、反向传播、梯度计算、链式法则及注意事项、损失函数等，并详细讲解了如何使用循环方式和矩阵方式进行模型构建及其实现。此外，我还阅读了《机器学习全面指南》，该书专注于相关主题背后的数学和理论。书中涉及卷积神经网络及其各层、池化层、CNN的反向传播、ResNet以及对CNN的直观理解等内容。除此之外，我还观看了几段关于神经网络和深度学习的视频。我在截图中展示了使用TensorFlow的Functional API和Sequential API实现的简单神经网络。希望你也花些时间阅读上述主题和书籍。对未来几天充满期待！！\n- 书籍：\n  - [**从零开始的机器学习**](https:\u002F\u002Fdafriedman97.github.io\u002Fmlbook\u002Fcontent\u002Fintroduction.html)\n  - **机器学习全面指南**\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_00dca87ea7ff.png)\n\n**300天数据之旅第9天！**\n- **强化学习**：在强化学习中，被称为“智能体”的学习系统可以在特定环境中观察环境状态，选择并执行动作，从而获得奖励或因负面行为而受到惩罚。它必须通过自我学习，找到一种能够随着时间推移获得最大累积奖励的最佳策略。在我的机器学习和深度学习之旅中，今天我开始阅读并实践《动手学机器学习：基于Scikit-Learn、Keras和TensorFlow》一书的内容。书中简要介绍了机器学习的全景，包括监督学习、无监督学习、半监督学习、强化学习、批处理学习与在线学习、基于实例的学习与基于模型的学习等不同类型的学习系统。我在截图中展示了使用Python实现的简单线性回归和K近邻算法，并附带了一个简单的图表。希望你也花些时间阅读上述主题和书籍。对未来几天充满期待！！\n- 书籍：\n  - **动手学机器学习：基于Scikit-Learn、Keras和TensorFlow**\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2980f7cd451f.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_be833d8d9e75.png)\n\n**300天数据之旅第10天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读了关于机器学习主要挑战的内容，包括训练数据不足、训练数据缺乏代表性、数据质量差、特征无关、过拟合与欠拟合、数据集划分中的问题、超参数调优与模型选择以及数据不匹配等问题，内容来自《动手学机器学习：基于Scikit-Learn、Keras和TensorFlow》一书。随后，我开始着手处理该书中包含的“加州房价”数据集，在这个项目中我将构建一个加州房价预测模型。我在截图中展示了使用Python进行数据处理的简单实现以及一些探索性数据分析技术。此外，我还演示了使用Sweetviz库进行数据分析的方法。非常感谢Chanin Nantasenamat在其视频中分享了关于该库的信息。希望你也花些时间阅读上述主题和书籍。对未来几天充满期待！！\n- 书籍：\n  - **动手学机器学习：基于Scikit-Learn、Keras和TensorFlow**\n- [**Chanin Nantasenamat关于Sweetviz的视频**](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UR_OK8vBpeY&lc=z22itptbrzv0vfky504t1aokgq4l23pa5kermfzdyrfkbk0h00410.1605764911555430)\n- [**加州房价数据集**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4d093990331c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_daf1d3e8afae.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_30cc9b31daf9.png)\n\n**300天数据之旅第11天！**\n- 在我的机器学习和深度学习之旅中，今天我学习并实践了从属性创建类别、分层抽样、通过可视化数据获取洞察、散点图、相关性、散点矩阵以及属性组合等内容，这些内容均来自《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书。我继续使用该书中包含的“加州房价”数据集进行练习。该数据集基于1990年加州人口普查的数据。在这个项目中，我将构建一个加州房价预测模型。目前我仍在继续推进这个项目。在这里的截图中，我展示了使用Python实现的分层抽样、利用散点矩阵计算相关性以及属性组合的方法。此外，我还展示了使用散点图计算相关性的截图。希望大家也能花些时间来研究这些内容，并阅读上述书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n- [**加州房价**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_efcb602aa8be.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_14e0220ad620.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c81581a794dc.png)\n\n**300天数据之旅第12天！**\n- 在我的机器学习和深度学习之旅中，今天我学习并实践了为机器学习算法准备数据、数据清洗、简单插补器、序数编码器、独热编码器、特征缩放、转换管道、标准化缩放器、列变换器、线性回归、决策树回归器以及交叉验证等内容，这些内容均来自《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书。我继续使用该书中包含的“加州房价”数据集进行练习。该数据集基于1990年加州人口普查的数据。在这个项目中，我将构建一个加州房价预测模型。笔记本中几乎涵盖了上述所有主题。在这里的截图中，我展示了使用Python实现的数据准备、缺失值处理、独热编码器、列变换器、线性回归、决策树回归器以及交叉验证的过程。希望大家也能花些时间来研究这些内容，并阅读上述书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n- [**加州房价**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_974b8e02957b.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b1bada89ff4d.png)\n\n**300天数据之旅第13天！**\n- 在我的机器学习和深度学习之旅中，今天我学习并实践了随机森林回归器、集成学习、模型调优、网格搜索、随机搜索、分析最佳模型及误差、模型评估、交叉验证等与之相关的主题，这些内容均来自《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书。我已经完成了对该书中包含的“加州房价”数据集的处理。该数据集基于1990年加州人口普查的数据。我使用随机森林回归器构建了一个加州房价预测模型，用于预测加州房屋的价格。在这里的截图中，我展示了使用Python实现的随机森林回归器、通过网格搜索和随机搜索进行模型调优以及交叉验证的过程。希望大家也能花些时间来研究这些内容，并阅读上述书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n- [**加州房价**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices.git)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c4d1f124a88a.png)\n\n**300天数据之旅第14天！**\n- **混淆矩阵**：混淆矩阵是评估分类器性能的一种更有效的方法。其基本思想是统计属于类别A的样本被错误地分类为类别B的次数。这种方法需要有一组预测结果，以便将其与实际标签进行比较。在我的机器学习和深度学习之旅中，今天我阅读并实践了分类、使用随机梯度下降训练二分类器、通过交叉验证衡量准确率、交叉验证的实现、混淆矩阵、精确率和召回率及其曲线等相关内容，这些内容均来自《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书。在这里的截图中，我展示了在MNIST数据集中使用SGD分类器进行分类，并计算精确率和召回率的过程。同时，我还展示了精确率和召回率的曲线。希望大家也能花些时间来研究这些内容，并阅读上述书籍。我对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f84241932f38.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_70ce33a06385.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f8868541c0ef.png)\n\n**300天数据之旅第15天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了ROC曲线、随机森林分类器、SGD分类器、多分类问题、一对一和一对多策略、交叉验证、使用混淆矩阵进行误差分析、K近邻分类器、多输出分类、噪声、精确率与召回率的权衡等主题，内容均来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我已经完成了这本书中的“分类”章节。我在截图中展示了ROC曲线、随机森林分类器在多分类中的应用、一对一策略、标准化缩放、误差分析、多标签分类以及使用Scikit-Learn实现的多输出分类的代码实现。希望你也能够尝试这些内容，并花些时间阅读上述主题和书籍。我对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ac5fe1d24fd1.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_abcc18e1a26a.png)\n\n**300天数据之旅第16天！**\n- **岭回归**：岭回归是一种正则化的线性回归方法，即在损失函数中加入一个正则化项，迫使学习算法不仅拟合数据，还要尽可能地使模型权重保持较小。在我的机器学习和深度学习之旅中，今天我阅读并实践了模型训练、线性回归、正规方程及其计算复杂度、损失函数与梯度下降（包括批量梯度下降、收敛速度、随机梯度下降、小批量梯度下降）、多项式回归及多项式特征、学习曲线、偏差与方差的权衡、正则化线性模型（如岭回归）等内容，这些内容同样来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了多项式回归、学习曲线和岭回归的实现，并用Python进行了可视化。希望你能花些时间去实践这些内容，并阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_74135584226e.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d0eef550c50c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6869ef08dc6c.png)\n\n**300天数据之旅第17天！**\n- **弹性网络**：弹性网络介于岭回归和Lasso回归之间。其正则化项**r**是岭回归和Lasso回归正则化项的简单混合。当**r**等于0时，它等同于岭回归；当**r**等于1时，它等同于Lasso回归。在我的机器学习和深度学习之旅中，今天我阅读并实践了Lasso回归、弹性网络、早停法、SGD回归器、逻辑回归、概率估计、训练与损失函数、Sigmoid函数、决策边界、Softmax回归或多项式逻辑回归、交叉熵等相关主题，内容均来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我刚刚开始阅读支持向量机这一主题。我在截图中展示了使用Scikit-Learn实现的Lasso回归、弹性网络、早停法、逻辑回归和Softmax回归的简单代码。希望你能花些时间去实践这些内容，并阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8f9360aab3bf.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e6fb40994b58.png)\n\n**300天数据之旅第18天！**\n- **支持向量机**：支持向量机（SVM）是一种功能强大且用途广泛的机器学习模型，能够执行线性和非线性分类、回归，甚至异常值检测。SVM特别适合处理复杂但规模中等的数据集的分类任务。在我的机器学习和深度学习之旅中，今天我阅读并实践了支持向量机、线性SVM分类、软间隔分类、非线性SVM分类、多项式回归、多项式核、添加相似性特征、高斯RBF核、计算复杂度、SVM回归（包括线性和非线性）等相关内容，这些内容同样来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了使用SVC和Linear SVC实现的非线性SVM分类，并用Python进行了可视化。希望你能花些时间去实践这些内容，并阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2d7ca6f64985.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c6c928e6ab64.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c6e9f1f561f8.png)\n\n**300天数据之旅第19天！**\n- **投票分类器**：投票分类器是指将多个不同分类器的预测结果进行集成，最终以得票最多的类别作为预测结果的分类器。其中，多数投票的分类器称为硬投票分类器。在我的机器学习和深度学习之旅中，今天我阅读并实践了集成学习与随机森林、投票分类器（包括硬投票和软投票分类器）等相关内容。实际上，我也已经和一支优秀的团队一起开始了研究项目。我在截图中展示了使用Scikit-Learn实现的硬投票和软投票分类器的代码。希望你能花些时间去实践这些内容，并阅读上述主题。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1fe45564bdaf.png)\n\n**300天数据之旅第20天！**\n- **CART训练算法**：这是Scikit Learn中用于实现分类与回归树（CART）训练算法的实现，用来训练决策树，也称为生长树。其工作原理是利用某个特征和阈值将训练集划分为两个子集。在我的机器学习和深度学习之旅中，今天我阅读并实践了关于决策函数与预测、决策树、决策树分类器、做出预测、基尼不纯度、白盒模型与黑盒模型、类别概率估计、CART训练算法、计算复杂度、熵、正则化超参数、决策树回归器、损失函数以及不稳定性等内容，这些内容均来自书籍《使用Scikit Learn、Keras和TensorFlow动手学机器学习》。我在这里通过Python实现了决策树分类器和决策树回归器的简单应用，并进行了可视化展示。希望大家也能花些时间动手实践，并阅读上述主题及书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_954becc80ea8.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_863f97343bac.png)\n\n**300天数据之旅第21天！**\n- **Bagging与Pasting**：这是一种方法，它对每个预测器使用相同的训练算法，但在训练时会从训练集中抽取不同的随机子集。如果抽样是有放回的，则称为Bagging；如果是无放回的，则称为Pasting。在我的机器学习和深度学习之旅中，今天我阅读并实践了关于集成学习与随机森林、投票分类器、Scikit Learn中的Bagging与Pasting、袋外评估、随机补丁与随机子空间、随机森林、极端随机树集成、特征重要性、提升方法、AdaBoost、梯度提升等主题，这些内容同样来自书籍《使用Scikit Learn、Keras和TensorFlow动手学机器学习》。我在这里通过Python实现了Bagging集成、决策树、随机森林分类器、特征重要性、AdaBoost分类器以及梯度提升，并附上了截图。希望大家也能花些时间动手实践，并阅读上述主题及书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ca918300ee73.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e49b706659b4.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_239a06cb4126.png)\n\n**300天数据之旅第22天！**\n- **流形学习**：流形学习是指一类降维算法，它们通过建模训练样本所处的流形来工作，这依赖于流形假设——即大多数现实世界中的高维数据集实际上位于一个低得多的维数流形上。在我的机器学习和深度学习之旅中，今天我阅读并实践了关于梯度提升、早停法、随机梯度提升、极端梯度提升（XGBoost）、堆叠与融合、降维、维度灾难、降维方法、投影与流形学习等内容，这些内容同样来自书籍《使用Scikit Learn、Keras和TensorFlow动手学机器学习》。我在这里通过Scikit Learn实现了带有早停法的梯度提升，并进行了可视化展示。希望大家也能花些时间动手实践，并阅读上述主题及书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fb432add10e8.png)\n\n**300天数据之旅第23天！**\n- **增量主成分分析**：增量PCA或IPCA算法允许我们将训练集拆分成多个小批量，然后每次只输入一个小批量给IPCA算法处理。这种方法对于大型训练集非常有用，同时也适用于在线应用PCA。在我的机器学习和深度学习之旅中，今天我阅读并实践了关于主成分分析（PCA）、保持方差、主成分、降维投影、解释方差比例、选择合适的维度数量、PCA在压缩与解压缩中的应用、重构误差、随机PCA、奇异值分解（SVD）、增量PCA等内容，这些内容均来自书籍《使用Scikit Learn、Keras和TensorFlow动手学机器学习》。我在这里通过Scikit Learn实现了PCA、随机PCA和增量PCA，并附上了相应的可视化图示。希望大家也能花些时间动手实践这些内容，同时阅读上述主题及书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_956d2524e7a9.png)\n\n**300天数据之旅第24天！**\n- **聚类**：聚类算法的目标是将相似的实例分组到同一个簇中。它是数据分析、客户细分、推荐系统、搜索引擎、图像分割、降维等领域的强大工具。在我的机器学习和深度学习之旅中，今天我阅读并实现了核主成分分析、核函数的选择与超参数调优、管道与网格搜索、局部线性嵌入、多维尺度分析、Isomap和线性判别分析等降维技术，以及无监督学习中的聚类和K均值聚类算法等内容，这些内容均来自《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》一书。我在截图中展示了核PCA、网格搜索交叉验证以及K均值聚类算法的实现，并用Python进行了可视化。希望大家也能花些时间实践这些内容，并阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c5ecb84c4ff2.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b49645227ab4.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d3700add91f5.png)\n\n**300天数据之旅第25天！**\n- **图像分割**：图像分割是将图像划分为多个区域的任务。在语义分割中，属于同一类对象的所有像素会被分配到同一个区域；而在实例分割中，属于单个对象的所有像素则会被分配到同一个区域。在我的机器学习和深度学习之旅中，今天我阅读并实现了K均值算法、质心初始化、加速版K均值和小批量K均值、寻找最佳簇数（肘部法则与轮廓系数）、K均值的局限性、利用聚类进行图像分割及预处理（如降维）等内容，这些内容同样来自《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》一书。我在截图中展示了用于图像分割和预处理的聚类算法实现，并用Python进行了可视化。希望大家也能花些时间实践这些内容，并阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_68f4edcd368f.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_835d7f1d699c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ceafa3d7b88.png)\n\n**300天数据之旅第26天！**\n- **高斯混合模型**：高斯混合模型是一种概率模型，它假设数据是由多个未知参数的高斯分布混合生成的。每个由单一高斯分布生成的数据点会形成一个通常呈椭球形的簇。在我的机器学习和深度学习之旅中，今天我阅读并实现了利用聚类算法进行半监督学习、主动学习与不确定性采样、DBSCAN、凝聚聚类、BIRCH算法、均值漂移与亲和力传播算法、谱聚类、高斯混合模型、期望最大化算法等内容，这些内容均来自《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》一书。我在截图中展示了用于半监督学习和DBSCAN的聚类算法实现，并用Python进行了可视化。希望大家也能花些时间实践这些内容，并阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_77f898c5d789.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_357d5fb16491.png)\n\n**300天数据之旅第27天！**\n- **异常检测**：异常检测又称离群点检测，是指识别那些严重偏离正常模式的实例。这些偏离正常模式的实例被称为异常或离群点，而正常的实例则称为内点。异常检测在欺诈检测等领域非常有用。在我的机器学习和深度学习之旅中，今天我阅读并实现了高斯混合模型、基于高斯混合模型的异常检测、新奇性检测、簇数的选择、贝叶斯信息准则、赤池信息准则、似然函数、贝叶斯高斯混合模型、快速MCD、孤立森林、局部离群因子、一类支持向量机等内容，这些内容同样来自《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》一书。我刚刚开始从这本书中学习神经网络和深度学习。我在截图中展示了高斯混合模型的实现，并用Python进行了可视化。希望大家也能花些时间实践这些内容，并阅读上述主题和书籍。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit Learn、Keras和TensorFlow动手实践机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d462154b926e.png)\n\n**300天数据之旅第28天！**\n- **修正线性单元函数或ReLU**：它是一个连续函数，但在0点处不可导，因为斜率在此处发生突变，导致梯度下降法在该点附近来回震荡。尽管如此，ReLU表现非常好，并且计算速度快。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书中关于人工神经网络简介、生物神经元、神经元的逻辑运算、感知机、赫布学习、多层感知机与反向传播、梯度下降、双曲正切函数和修正线性单元函数、回归型MLP、分类型MLP、Softmax激活函数等内容。我在快照中展示了使用Sequential API构建图像分类器的实现，并结合Keras进行了可视化。希望大家也能花些时间动手实践这些内容，并阅读上述书籍及相关主题。我对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b7796ddb42f5.png)\n\n**300天数据之旅第29天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书中关于使用Sequential API创建模型、编译模型、损失函数与激活函数、训练与评估模型、学习曲线、利用模型进行预测、使用Sequential API构建回归型MLP、使用Functional API构建复杂模型、深度神经网络等内容。我在快照中展示了使用Sequential API和Functional API构建回归型MLP的实现。希望大家能从中获得一些启发，并花时间动手实践。同时，也建议大家抽出时间阅读并实践上述书籍中的相关内容。我对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9eb094d5a2ea.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ce434d3cbc9f.png)\n\n**300天数据之旅第30天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书中关于使用Functional API构建复杂模型、深度神经网络架构、ReLU激活函数、处理模型中的多个输入、均方误差损失函数和随机梯度下降优化器、处理多个输出或用于正则化的辅助输出等内容。我在快照中展示了使用Keras Functional API处理多个输入，以及使用相同方法处理多个输出或用于正则化的辅助输出的实现。希望大家能从中获得一些见解，并加以实践。同时也建议大家花些时间阅读上述及下方提到的书籍中的相关内容。我对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8f638beb5a1e.png)\n\n**300天数据之旅第31天！**\n- **回调与早停**：早停是一种方法，允许你指定任意数量的训练轮次，一旦模型在验证集上的表现不再提升，便会自动停止训练。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书中关于使用子类化API、Sequential API和Functional API构建动态模型、保存与恢复模型、使用回调、模型检查点、早停、Weights & Biases等内容。我在快照中展示了使用子类化API构建动态模型，以及使用回调和早停的实现。希望大家能从中获得一些启发，并加以实践。同时也建议大家花些时间阅读上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bf4978ac651c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_19d0c6663fcd.png)\n\n**300天数据之旅第32天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》一书中关于使用TensorBoard进行可视化、学习曲线、微调神经网络超参数、随机搜索交叉验证、回归器、用于优化超参数的库如Hyperopt、Talos等、隐藏层数量、每层隐藏神经元数量、学习率、批量大小及其他超参数等内容。此外，我还花了一段时间阅读了一篇名为《基于梯度的深度架构训练实用建议》的论文。在这篇文章中，我了解了深度学习与贪心式逐层预训练、在线学习以及泛化误差的优化等相关内容。我在快照中展示了超参数调优、Keras回归器和随机搜索交叉验证的实现。希望大家能从中获得一些启发，并加以实践。同时也建议大家花些时间阅读上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Scikit-Learn、Keras和TensorFlow动手学机器学习》\n- 论文：\n  - [《基于梯度的深度架构训练实用建议》](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1206.5533.pdf)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f5eaf5fa6e77.png)\n\n**300天数据之旅第33天！**\n- **梯度消失问题**：在反向传播和计算梯度的过程中，随着算法不断向深层传递，梯度往往会越来越小，从而阻碍训练收敛到较好的解。这便导致了梯度消失问题。在我的机器学习与深度学习学习旅程中，今天我阅读并实践了深度神经网络的训练、梯度消失与爆炸问题、Glorot和He初始化、非饱和激活函数、批量归一化及其实现、逻辑斯谛与Sigmoid激活函数、SELU激活函数、ReLU激活函数及其变体、Leaky ReLU和参数化Leaky ReLU等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了Leaky ReLU和批量归一化的实现。希望你能从中获得一些启发，并进一步实践。也建议你花些时间阅读上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6ec626fe9e93.png)\n\n**300天数据之旅第34天！**\n- **梯度裁剪**：梯度裁剪是一种缓解梯度爆炸问题的技术，它通过在反向传播过程中对梯度进行裁剪，使其不超过某个阈值，通常用于循环神经网络中。在我的机器学习与深度学习学习旅程中，今天我阅读并实践了梯度裁剪、批量归一化、复用预训练层、深度神经网络与迁移学习、无监督预训练、受限玻尔兹曼机、辅助任务上的预训练、自监督学习、更快的优化器、梯度下降优化器、动量优化、Nesterov加速梯度等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了使用Keras和Sequential API进行迁移学习的简单实现。希望你能从中获得一些启发，并进一步实践。也建议你花些时间阅读上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0883dbea8d95.png)\n\n**300天数据之旅第35天！**\n- **Adam优化器**：Adam即自适应矩估计，结合了动量优化和RMSProp的思想——动量优化会跟踪过去梯度的指数衰减平均值，而RMSProp则跟踪过去平方梯度的指数衰减平均值。在我的机器学习与深度学习学习旅程中，今天我阅读并实践了AdaGrad算法、梯度下降法、RMSProp算法、自适应矩估计（即Adam优化器）、Adamax、Nadam优化、稀疏模型的训练、双重平均法、学习率调度、幂次调度、指数调度、分段常数调度、性能调度等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了指数调度和分段常数调度的实现。希望你能从中获得一些启发，并进一步实践。也建议你花些时间阅读上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5e3f67349315.png)\n\n**300天数据之旅第36天！**\n- **深度神经网络**：在大多数情况下无需过多超参数调优即可良好工作的最佳深度神经网络配置如下：卷积核初始化采用LeCun初始化，激活函数选用SELU，不进行归一化处理，使用早停法作为正则化手段，优化器选择Nadam，学习率调度采用性能调度。在我的机器学习与深度学习学习旅程中，今天我阅读并实践了通过正则化避免过拟合、L1和L2正则化、Dropout正则化、自归一化、批量归一化、蒙特卡洛Dropout、最大范数正则化、SELU和Leaky ReLU等激活函数、Nadam优化等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了使用Keras实现L2正则化和Dropout正则化的代码。希望你能从中获得一些启发，并进一步实践。也建议你花些时间阅读上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_42473506a7b7.png)\n\n**300天数据之旅第37天！**\n- 在我的机器学习与深度学习学习旅程中，今天我阅读并实践了使用TensorFlow自定义模型及训练、高级深度学习API、输入输出与预处理、低级深度学习API、部署与优化、TensorFlow架构、张量与运算、Keras低级API、张量与NumPy、稀疏张量、数组、字符串张量、自定义损失函数、保存与加载包含自定义组件的模型等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。此外，我还开始阅读《语音与语言处理》一书。书中涉及正则表达式、文本标准化、分词、词形还原、词干提取、句子分割、编辑距离等内容。我在截图中展示了自定义损失函数的简单实现。希望你也花些时间阅读上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n  - 《语音与语言处理》[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7a0411dcb0c1.png)\n\n**300天数据之旅第38天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了自定义激活函数、初始化器、正则化器与约束条件、自定义指标、MAE和MSE、流式指标、自定义层、自定义模型、基于模型内部机制的损失函数与指标，以及《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书中与此相关的其他主题。此外，我还开始阅读《语音与语言处理》这本书。在这一部分，我学习了正则表达式、基本正则表达式模式、或运算、范围、克莱尼星号、通配符表达式、分组与优先级、运算符层级、贪婪匹配与非贪婪匹配、序列与锚点、计数器等主题。我在截图中展示了自定义激活函数、初始化器、正则化器、约束条件和自定义指标的实现。希望你也花些时间阅读上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n  - 《语音与语言处理》（[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_db08b8aa4203.png)\n\n**300天数据之旅第39天！**\n- **预取与数据API**：预取是指在资源被需要之前就将其加载，以减少等待该资源的时间。换句话说，当训练算法正在处理一个批次时，数据集会同时并行准备下一个批次，从而显著提升性能。在我的机器学习和深度学习之旅中，今天我阅读并实践了使用TensorFlow加载和预处理数据、数据API、转换链式操作、数据集打乱、梯度下降、多文件行间交错、并行处理、数据集预处理、解码、预取、多线程等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了使用TensorFlow实现数据API的简单示例。希望你能从中获得一些启发，并进一步实践。也希望大家能抽出时间阅读上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_255691cdb16c.png)\n\n**300天数据之旅第40天！**\n- **嵌入与表示学习**：嵌入是一个可训练的稠密向量，用于表示某个类别。类别的表示越准确，神经网络就越容易做出精确的预测，因此嵌入必须能够有效地表示各类别。这被称为表示学习。在我的机器学习和深度学习之旅中，今天我阅读并实践了特征API、列变换器、数值与类别特征、交叉类别特征、使用独热编码和嵌入进行类别特征编码、表示学习、词嵌入、使用特征列进行解析、在模型中使用特征列等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了在数值和类别列中使用特征API进行解析的简单实现。希望你也花些时间阅读上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fc0c1c2de986.png)\n\n**300天数据之旅第41天！**\n- **卷积层**：卷积神经网络中最关键的构建模块就是卷积层。第一层卷积神经网络中的神经元并非与输入图像的每个像素相连，而只与其感受野内的像素相连。同样地，第二层卷积神经网络中的每个神经元也只与第一层内一个小矩形区域内的神经元相连。在我的机器学习和深度学习之旅中，今天我阅读并实践了使用卷积神经网络进行深度计算机视觉、视觉皮层的结构、卷积层、零填充、滤波器、多特征图堆叠、填充、内存需求、池化层、不变性、卷积神经网络架构等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了卷积神经网络架构的简单实现。希望你能从中获得一些启发，并继续深入研究。也希望你能抽出时间阅读上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_293764aa432e.png)\n\n**300天数据之旅第42天！**\n- **ResNet模型**：残差网络（ResNet）由何凯明开发，凭借一个由152层组成的超深卷积神经网络赢得了2015年ILSVRC挑战赛冠军。该网络使用跳跃连接，也称为捷径连接：输入到某一层的信号也会被加到稍靠上方的一层输出上。在我的机器学习和深度学习之旅中，今天我阅读并实践了LeNet-5架构、AlexNet卷积神经网络架构、数据增强、局部响应归一化、GoogLeNet架构、Inception模块、VGGNet、残差网络（ResNet）、残差学习、Xception或极端Inception、挤压与激励网络（SENet）等内容，这些都来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了使用Keras实现ResNet 34卷积神经网络的过程。希望你能从中获得一些启发，并继续深入研究。也希望你能抽出时间阅读上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e88d2ac6b72b.png)\n\n**第43天，300天数据之旅！**\n- **Xception模型**：Xception是Extreme Inception的缩写，是GoogLeNet架构的一种变体，由François Chollet于2016年提出。它融合了GoogLeNet和ResNet架构的思想，但用一种称为深度可分离卷积的特殊层替换了Inception模块。在我的机器学习和深度学习之旅中，今天我阅读并实践了关于使用Keras中的预训练模型、GoogLeNet和残差网络（ResNet）、ImageNet、用于迁移学习的预训练模型、Xception模型、卷积神经网络、批处理、预取、全局平均池化等内容，这些内容均来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我在截图中展示了ResNet和Xception等预训练模型在迁移学习中的实现。希望你能从中获得一些启发，并进一步深入研究。也希望大家能花些时间阅读上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b1c85638d98d.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f34eb6c5db76.png)\n\n**第44天，300天数据之旅！**\n- **语义分割**：在语义分割中，每个像素都会根据其所属物体的类别进行分类，但同一类别的不同物体不会被区分开来。在我的机器学习和深度学习之旅中，今天我阅读并实践了关于分类与定位、计算机视觉中的众包、交并比指标、目标检测、全卷积网络（FCN）、VALID填充、YOLO架构、平均精度均值（MAP）、卷积神经网络、语义分割等内容，这些内容均来自《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》一书。我刚刚完成了这本书的学习。我在截图中展示了分类与定位的实现以及相应的可视化效果。希望你能从中获得一些见解，并继续深入研究。也希望大家能花些时间阅读上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学机器学习：使用Scikit-Learn、Keras和TensorFlow》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_93e80d5a5d0c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_363a3606e74b.png)\n\n**第45天，300天数据之旅！**\n- **经验风险最小化**：训练模型意味着从有标签的样本中学习所有权重和偏置的良好值。在监督学习中，机器学习算法通过检查大量样本，尝试找到一个能够最小化损失的模型，这一过程被称为经验风险最小化。在我的机器学习和深度学习之旅中，今天我开始学习Google的《机器学习速成课程》。在这里，我学习了机器学习哲学、机器学习基础及其应用、标签与特征、有标签与无标签样本、模型与推理、回归与分类、线性回归、权重与偏置、训练与损失、经验风险最小化、均方误差（MSE）、降低损失、梯度下降等内容。我在截图中展示了简单的基本循环神经网络的实现。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习上述及下方提到的课程内容。对接下来的日子充满期待！！\n- 课程：\n  - [**机器学习速成课程**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ae75d0887661.png)\n\n**第46天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我继续学习Google的《机器学习速成课程》。在这里，我学习并实践了关于学习率或步长、机器学习算法中的超参数、回归、梯度下降、优化学习率、随机梯度下降（SGD）、批次与批量大小、小批量随机梯度下降、收敛、TensorFlow工具包层次结构等内容。此外，我还花了一些时间阅读《语音与语言处理》一书。书中介绍了正则表达式与模式、精确率与召回率、克莱尼星号、常用字符的别名、用于计数的RE运算符等内容。我在截图中展示了使用Keras实现的简单循环神经网络和深度RNN。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习上述及下方提到的课程和书籍内容。对接下来的日子充满期待！！\n- 课程：\n  - [**机器学习速成课程**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n- 书籍：\n  - [**语音与语言处理**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8fab746b7f17.png)\n\n**第47天，300天数据之旅！**\n- **特征向量与特征工程**：特征工程是指将原始数据转换为特征向量，即构成数据集样本的一组浮点数值。在我的机器学习和深度学习之旅中，今天我继续学习Google的《机器学习速成课程》。在这里，我学习并实践了关于模型泛化、过拟合、梯度下降与损失、统计学习理论与计算学习理论、数据平稳性、数据拆分与验证集、表示与特征工程、特征向量、分类特征与词汇表、独热编码与稀疏表示、良好特征的特性等内容。我在截图中展示了RNN及其GRU单元的简单实现。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习上述及下方提到的课程和书籍内容。对接下来的日子充满期待！！\n- 课程：\n  - [**机器学习速成课程**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9e8d9bf7fc77.png)\n\n**300天数据之旅第48天！**\n- **特征缩放**：特征缩放是指将浮点型特征值从其自然范围转换到标准范围，例如0到1。如果特征集包含多个特征，那么特征缩放可以帮助梯度下降更快地收敛。在我的机器学习和深度学习之旅中，今天我学习了谷歌的《机器学习速成课程》。在这里，我学习并实现了特征值的缩放、极端异常值的处理、分箱、数据清洗、标准差、特征交叉与合成特征、非线性编码、随机梯度下降、交叉乘积、独热向量的交叉、用于简化的正则化、泛化曲线、L2正则化、早停法、Lambda参数与学习率等主题。我在截图中展示了使用Sequential API实现的简单线性回归模型。希望你能从中获得一些启发，并加以实践。我也希望你能花些时间学习上述及下方提到的课程内容。对接下来的日子充满期待！！\n- 课程：\n  - [**机器学习速成课程**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_381178f54eb1.png)\n\n**300天数据之旅第49天！**\n- **预测偏差**：预测偏差是用来衡量预测值的平均值与数据集中标签平均值之间差距的一个指标。预测偏差与偏置是完全不同的概念。在我的机器学习和深度学习之旅中，今天我同样学习了谷歌的《机器学习速成课程》。在这里，我学习并实现了逻辑回归与概率计算、Sigmoid函数、二分类、对数损失与正则化、早停法、L1和L2正则化、分类与阈值设定、混淆矩阵、类别不平衡与准确率、精确率与召回率、ROC曲线、曲线下面积（AUC）、预测偏差、校准层、分桶、稀疏性、特征交叉与独热编码等主题。我在截图中展示了使用Keras实现的归一化与二分类的简单案例。希望你能从中获得一些见解，并进一步实践。我也希望你能花些时间学习上述及下方提到的课程内容。对接下来的日子充满期待！！\n- 课程：\n  - [**机器学习速成课程**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f10c761ec855.png)\n\n**300天数据之旅第50天！**\n- **分类数据与稀疏张量**：分类数据指的是输入特征，这些特征代表来自有限选择集合中的一个或多个离散项。而稀疏张量则是指只有少量非零元素的张量。在我的机器学习和深度学习之旅中，今天我继续学习了谷歌的《机器学习速成课程》。在这里，我学习并实现了神经网络、隐藏层与激活函数、非线性分类与特征交叉、Sigmoid函数、修正线性单元（ReLU）、反向传播、梯度消失与爆炸问题、Dropout正则化、多分类神经网络、Softmax、逻辑回归、嵌入、协同过滤、稀疏特征、主成分分析、Word2Vec等主题。我在截图中展示了多分类任务中深度神经网络的简单实现。希望你能从中获得一些启发，并加以实践。我也希望你能花些时间学习上述及下方提到的课程内容。对接下来的日子充满期待！！\n- 课程：\n  - [**机器学习速成课程**](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cb16cd1a41f9.png)\n\n**300天数据之旅第51天！**\n- **深度学习**：深度学习是一类属于人工智能的算法，它通过提供指导性示例来训练称为深度神经网络的数学实体。深度学习利用大量数据来近似复杂的函数。在我的机器学习和深度学习之旅中，今天我开始阅读并实践《用PyTorch进行深度学习》这本书。在这里，我了解了PyTorch核心、深度学习简介与革命、张量与数组、深度学习的竞争格局、实用库、能够识别图像主体的预训练神经网络、ImageNet、图像识别、AlexNet与ResNet、Torch Vision模块等主题。我在截图中展示了使用PyTorch获取用于图像识别的预训练神经网络的实现。希望你能从中获得一些启发，并加以实践。我也希望你能花些时间学习上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**用PyTorch进行深度学习**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1d1125f28943.png)\n\n**300天数据之旅第52天！**\n- **GAN游戏**：GAN代表生成对抗网络，“生成”意味着创造，“对抗”表示两个神经网络在竞争以智胜对方，“网络”则指神经网络。循环GAN可以在无需显式提供训练集中匹配对的情况下，将一个领域的图像转换为另一个领域的图像。在我的机器学习和深度学习之旅中，今天我继续阅读并实践《用PyTorch进行深度学习》这本书。在这里，我学习了预训练模型、生成对抗网络（GAN）、ResNet生成器与判别器模型、循环GAN架构、Torch Vision模块、深度伪造技术、以及能将马变成斑马的神经网络等主题。我在截图中展示了使用PyTorch实现的将马变成斑马的循环GAN。希望你能从中获得一些启发，并加以实践。我也希望你能花些时间学习上述及下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**用PyTorch进行深度学习**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7c6fda44cc9a.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_04cdcfd8f5c0.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1be073233530.png)\n\n**300天数据之旅第53天！**\n- **张量与多维数组**：张量是PyTorch中的基本数据结构。张量是一种数组，它是一种存储数字集合的数据结构，这些数字可以通过索引单独访问，并且可以使用多个索引来进行索引。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用PyTorch进行深度学习》这本书的内容。在这里，我学习了描述场景的预训练神经网络——NeuralTalk2模型、循环神经网络、Torch Hub、基础构建块：张量、将世界视为浮点数、多维数组与张量、列表与张量索引、命名张量、爱因斯坦求和约定、广播等主题。我在这里通过截图展示了使用PyTorch对张量索引和命名张量的简单实现。希望你能从中获得一些启发，并进一步深入研究。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_56e8d6db2840.png)\n\n**300天数据之旅第54天！**\n- **张量与多维数组**：张量是PyTorch中的基本数据结构。张量是一种数组，它是一种存储数字集合的数据结构，这些数字可以通过索引单独访问，并且可以使用多个索引来进行索引。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用PyTorch进行深度学习》这本书的内容。在这里，我学习了命名张量、更改命名张量的名称、张量广播、无名维度、张量元素类型、指定数值数据类型、张量API、创建操作、索引、随机采样、序列化、并行计算、张量存储、引用存储、对存储进行索引等主题。我在这里通过截图展示了使用PyTorch对命名张量、张量数据类型属性以及张量API的简单实现。希望你能从中获得一些启发，并进一步深入研究。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d62469deb00c.png)\n\n**300天数据之旅第55天！**\n- **颜色通道编码**：将颜色编码为数字最常见的方式是RGB模式，其中一种颜色由三个数字表示，分别代表红色、绿色和蓝色的强度。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用PyTorch进行深度学习》这本书的内容。在这里，我学习了张量元数据，如大小、偏移量和步幅、不复制地转置张量、高维空间中的转置、连续张量、管理张量设备属性（如在GPU和CPU之间切换）、NumPy互操作性、广义张量、张量序列化、用张量表示数据、处理图像、添加颜色通道、改变布局等主题。我在这里通过截图展示了使用PyTorch处理图像的实现，包括改变布局和使用Permute方法以及连续张量的操作。希望你能从中获得一些启发，并进一步深入研究。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3065aecafccd.png)\n\n**300天数据之旅第56天！**\n- **连续值、有序值和分类值**：连续值是可以用单位进行计数和测量的数值。有序值是连续值的一种，但其值之间没有固定的相互关系。分类值则是可能性的枚举。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用PyTorch进行深度学习》这本书的内容。在这里，我学习了图像数据的归一化、处理3D图像或体积图像数据、表示表格数据、使用NumPy加载数据张量、连续值、有序值、分类值、比率尺度和区间尺度、名义尺度、独热编码与嵌入、单例维度等主题。我在这里通过截图展示了使用PyTorch对图像数据归一化、体积数据、表格数据以及独热编码的实现。希望你能从中获得一些启发，并进一步深入研究。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_45e72e057a9d.png)\n\n**300天数据之旅第57天！**\n- **连续值、有序值和分类值**：连续值是可以用单位进行计数和测量的数值。有序值是连续值的一种，但其值之间没有固定的相互关系。分类值则是可能性的枚举。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用PyTorch进行深度学习》这本书的内容。在这里，我学习了连续数据与分类数据、PyTorch张量API、在表格数据中寻找阈值、高级索引、处理时间序列数据、在数据中添加时间维度、按时间段塑造数据、张量与数组等主题。我在这里通过截图展示了使用PyTorch处理分类数据、时间序列数据以及寻找阈值的实现。希望你能从中获得一些启发，并进一步深入研究。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0e5e5321714f.png)\n\n**300天数据之旅第58天！**\n- **编码与ASCII**：每个书面字符都由一个代码表示，该代码对应一段适当长度的二进制序列，以便唯一地标识每个字符，这种表示方法称为编码。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了时间序列数据处理、有序变量、独热编码与拼接、unsqueeze与单例维度、均值、标准差及变量重缩放、文本表示、自然语言处理与循环神经网络、将文本转换为数字、Project Gutenberg语料库、字符的独热编码、编码与ASCII、嵌入以及文本处理等相关主题。我在截图中展示了使用PyTorch实现的时间序列数据和文本表示。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ef957bab8bf2.png)\n\n**300天数据之旅第59天！**\n- **损失函数**：损失函数是一种计算单一数值的函数，学习过程会尝试最小化这个数值。损失的计算通常涉及比较某些训练样本的期望输出与实际输出之间的差异。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了独热编码与向量、使用张量表示数据、文本嵌入、自然语言处理、学习机制、开普勒的建模启示、离心率、参数估计、权重、偏置与梯度、简单线性模型、损失函数或代价函数、均方损失、广播等主题。我在截图中展示了使用PyTorch实现的文本表示、学习机制和简单线性模型的简单示例。希望你能从中获得一些见解，并进一步探索。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2dad74c7a917.png)\n\n**300天数据之旅第60天！**\n- **梯度下降法**：梯度下降法是一种一阶迭代优化算法，用于寻找可微函数的局部最小值。简单来说，梯度就是函数关于各个参数的导数。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了代价函数或损失函数、利用梯度下降法优化参数、降低损失函数、参数估计、学习机制、缩放因子与学习率、模型评估、计算损失函数和线性函数的导数、定义梯度函数、偏导数以及迭代模型、训练循环等相关主题。我在截图中展示了损失函数、导数计算、梯度函数和训练循环的实现。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fbe232c155fe.png)\n\n**300天数据之旅第61天！**\n- **超参数调优**：超参数调优是指在训练过程中调整模型的参数和控制训练方式的超参数。超参数通常是手动设置的。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了梯度下降法、优化训练循环、过拟合、收敛与发散、学习率、超参数调优、输入归一化、数据可视化或绘图、参数解包、PyTorch的自动求导与反向传播、链式法则、线性模型等相关主题。我在截图中展示了使用PyTorch实现的训练循环、梯度下降法以及数据可视化等简单示例。希望你能从中获得一些启发，并继续探索。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4afcb6129223.png)\n\n**300天数据之旅第62天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了梯度下降法、PyTorch的自动求导与反向传播、链式法则与张量、grad属性与参数、简单线性函数与简单损失函数、累积梯度、清零梯度、启用自动求导的训练循环、优化器以及Torch的Optim子模块等相关主题。我在截图中展示了使用PyTorch实现的简单线性模型与损失函数、启用自动求导的训练循环。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ac2649d59ca.png)\n\n**300天数据之旅第63天！**\n- **随机梯度下降**：随机梯度下降（SGD）之所以得名，是因为梯度通常是通过对所有输入样本的随机子集取平均来计算的。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了优化器、原始梯度下降优化、随机梯度下降、动量参数、小批量、学习率与参数、Optim模块、神经网络模型、Adam优化器、反向传播、权重优化、训练、验证与过拟合、评估训练损失、泛化到验证集、过拟合与正则化项等主题。我在截图中展示了SGD和Adam优化器的实现以及训练循环。这是前一张截图的延续。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9f4d35e984fb.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5a2e9b354ca4.png)\n\n**300天数据之旅第64天！**\n- **激活函数**：激活函数是非线性的，这使得整个网络能够近似更复杂的函数。它们是可微的，因此可以通过它们计算梯度。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我正在学习如何使用神经网络来拟合数据、人工神经元、学习过程与损失函数、非线性激活函数、权重与偏置、构建多层网络、理解误差函数、限制和压缩输出范围、Tanh和ReLU激活函数、选择激活函数、PyTorch的NN模块等主题。我在截图中展示了使用PyTorch实现的简单线性模型和训练循环。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_760518bba361.png)\n\n**300天数据之旅第65天！**\n- **激活函数**：激活函数是非线性的，这使得整个网络能够近似更复杂的函数。它们是可微的，因此可以通过它们计算梯度。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了PyTorch的NN模块、简单线性模型、输入数据的批处理、批次优化、均方误差损失函数、训练循环、神经网络、序列模型、Tanh激活函数、检查参数、权重与偏置、OrderedDict模块、与线性模型的对比、过拟合等主题。我在截图中展示了使用PyTorch实现的简单序列模型和OrderedDict子模块。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a1fc27b89c98.png)\n\n**300天数据之旅第66天！**\n- **计算机视觉**：计算机视觉是一门跨学科的科学领域，研究计算机如何从数字图像或视频中获取高层次的理解。它旨在理解和自动化人类视觉系统所能完成的任务。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我开始了“从图像中学习”这一新主题。我学习了简单的图像识别、CIFAR10小型图像数据集、Torch Vision模块、数据集类、可迭代数据集、Python图像处理库（PIL包）、数据集变换、数组与张量、Permute函数等主题。我在截图中展示了使用PyTorch实现的Torch Vision模块及CIFAR10数据集的简单应用。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dbae9ccf0d9c.png)\n\n**300天数据之旅第67天！**\n- **计算机视觉**：计算机视觉是一门跨学科的科学领域，研究计算机如何从数字图像或视频中获取高层次的理解。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了排列函数、数据归一化、堆叠、均值与标准差、Torch Vision模块及其子模块、CIFAR10数据集、PIL包、图像识别、数据集的构建、全连接神经网络模型的构建、序列模型、简单线性模型、分类与回归问题、独热编码与Softmax等主题。我在截图中展示了使用Torch Vision模块进行数据归一化、数据集构建以及神经网络模型实现的过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fbfab3cffe2a.png)\n\n**300天数据之旅第68天！**\n- **Softmax函数**：Softmax函数是一种将一个数值向量映射为另一个相同维度的向量的函数，其中输出值满足概率约束。Softmax是一个单调递增函数，输入中的较小值会对应输出中的较小值。在机器学习和深度学习的学习旅程中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在此过程中，我学习了如何将输出表示为概率、Softmax函数、PyTorch的NN模块、反向传播、分类损失函数、均方误差损失、负对数似然损失或NLL损失、Log Softmax函数、分类器的训练、随机梯度下降、超参数、小批量处理等主题。我在截图中展示了使用PyTorch实现的Softmax函数、构建神经网络模型以及训练循环。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1c3d91bd70e7.png)\n\n**300天数据之旅第69天！**\n- **交叉熵损失**：交叉熵损失是目标分布下预测分布的负对数似然值。Log Softmax函数与NLL损失函数的组合等价于直接使用交叉熵损失。在机器学习和深度学习的学习旅程中，今天我同样阅读并实践了《使用PyTorch进行深度学习》一书的内容。在此，我学习了梯度下降、小批量与数据加载器、随机梯度下降、神经网络模型、Log Softmax函数、NLL损失函数、交叉熵损失函数、可训练参数、权重与偏置、平移不变性、数据增强、Torch Vision及NN模块等相关内容。我在截图中展示了使用PyTorch构建深度神经网络、训练循环以及模型评估的实现过程。希望你能从中获得一些见解，并进一步实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a92abc4c8120.png)\n\n**300天数据之旅第70天！**\n- **平移不变性**：平移不变性使卷积神经网络对平移具有不变性，也就是说，即使输入图像发生平移，CNN仍然能够正确识别其所属类别。在机器学习和深度学习的学习旅程中，今天我继续阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我开始学习“利用卷积进行泛化”这一主题。我了解了卷积神经网络、平移不变性、权重与偏置、离散互相关、局部性或对邻域数据的局部操作、模型参数、多通道图像、边界填充、卷积核大小、用卷积检测特征等内容。我在截图中展示了使用PyTorch实现的简单CNN以及数据构建过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0651c18b3c1b.png)\n\n**300天数据之旅第71天！**\n- **下采样**：下采样是指将图像尺寸缩小一半的操作，相当于以四个相邻像素作为输入，生成一个输出像素。下采样的原理可以通过多种方式实现。在机器学习和深度学习的学习旅程中，今天我继续阅读并实践了《使用PyTorch进行深度学习》一书的内容。在此，我学习了卷积核大小、图像填充、边缘检测卷积核、局部性和平移不变性、学习率与权重更新、最大池化层与下采样、步幅、卷积神经网络、感受野、Tanh激活函数、简单线性模型、序列模型、模型参数等相关内容。我在截图中展示了使用PyTorch实现的卷积神经网络、图像绘制以及模型参数的检查过程。希望你能从中获得一些见解，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1e44aea17000.png)\n\n**300天数据之旅第72天！**\n- **下采样**：下采样是指将图像尺寸缩小一半的操作，相当于以四个相邻像素作为输入，生成一个输出像素。下采样的原理可以通过多种方式实现。在机器学习和深度学习的学习旅程中，今天我继续阅读并实践了《使用PyTorch进行深度学习》一书的内容。在此，我学习了NN模块的子类化、顺序式API与模块化API、前向传播函数、线性模型、最大池化层、数据填充、卷积神经网络架构、ResNet、卷积核大小及其属性、Tanh激活函数、模型参数、函数式API、无状态模块等相关内容。我在截图中展示了使用顺序式API和函数式API对NN模块进行子类化的实现过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c637bdb00a1b.png)\n\n**300天数据之旅第73天！**\n- **下采样**：下采样是将图像尺寸缩小一半的过程，相当于以四个相邻像素作为输入，生成一个输出像素。下采样的原理可以通过多种方式实现。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了Torch NN模块、函数式API、卷积神经网络及其训练过程、数据加载器模块、网络的前向传播与反向传播、随机梯度下降优化器、梯度清零、交叉熵损失函数、模型评估以及梯度下降等相关内容。我在截图中展示了使用PyTorch实现的训练循环和模型评估过程。实际上，这延续了昨天的截图内容。希望你能从中获得一些启发，并进一步实践。也期待你花时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_022f87f2e1e4.png)\n\n**300天数据之旅第74天！**\n- **下采样**：下采样是将图像尺寸缩小一半的过程，相当于以四个相邻像素作为输入，生成一个输出像素。下采样的原理可以通过多种方式实现，例如最大池化。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了模型、权重和参数的保存与加载、在GPU上训练模型、Torch NN模块及其子模块、映射位置关键字、模型设计、长短期记忆网络（LSTM）、为网络增加记忆容量或宽度、前馈网络、过拟合等问题。我在截图中展示了使用PyTorch为网络增加记忆容量或宽度的实现方法。希望你能从中获得一些启发，并继续深入研究。也期待你花时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_adc6195bf8a7.png)\n\n**300天数据之旅第75天！**\n- **L2正则化**：L2正则化是模型中所有权重平方的总和，而L1正则化则是所有权重绝对值的总和。L2正则化也被称为权重衰减。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了卷积神经网络、L2正则化和L1正则化、优化与泛化、权重衰减、PyTorch NN模块及其子模块、随机梯度下降优化器、过拟合与丢弃层、深度神经网络、随机化等相关内容。我在截图中展示了使用PyTorch实现L2正则化和丢弃层的方法。希望你能从中获得一些启发，并继续探索。也期待你花时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e0fad874ecc4.png)\n\n**300天数据之旅第76天！**\n- **L2正则化**：L2正则化是模型中所有权重平方的总和，而L1正则化则是所有权重绝对值的总和。L2正则化也被称为权重衰减。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了丢弃层模块、批量归一化和非线性激活函数、正则化与原则性增强、卷积神经网络、小批量和标准差、深度神经网络及深度模块、跳跃连接机制、ReLU激活函数、函数式API的实现等相关内容。我在截图中展示了使用PyTorch实现批量归一化、深度神经网络及深度模块的方法。希望你能从中获得一些启发，并继续深入研究。也期待你花时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f3149ebdfe17.png)\n\n**300天数据之旅第77天！**\n- **恒等映射**：当第一层激活的输出除了通过标准的前馈路径外，还被用作最后一层的输入时，就称为恒等映射。恒等映射可以缓解梯度消失的问题。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》一书的内容。在这里，我学习了卷积神经网络、跳跃连接、ResNet架构、简单线性层、最大池化层、恒等映射、高速公路网络、UNet模型、密集网络和超深度神经网络、序列式和函数式API、前向传播与反向传播、Torch Vision模块及其子模块、批量归一化层、自定义初始化等相关内容。我在截图中展示了使用PyTorch实现ResNet架构和超深度神经网络的方法。希望你能从中获得一些启发，并继续探索。也期待你花时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b4b7da562d22.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d545297596c4.png)\n\n**300天数据之旅第78天！**\n- **体素**：体素是大家熟悉的2D像素在3D空间中的对应物。它不是表示一个面积，而是表示一个体积。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》这本书的内容。在这里，我学习了CT扫描数据集、体素、分割、分组与分类、结节、3D卷积、神经网络、LUNA数据集的下载、数据加载、数据解析、训练集与验证集等主题。我还开始处理LUNA数据集，即2016年肺结节分析数据集。LUNA大型挑战赛结合了一个开放的数据集和高质量的患者CT扫描标签——其中许多包含肺结节，并且对基于该数据的分类器进行了公开排名。我在截图中展示了使用PyTorch准备数据的实现过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_39e3719ca4d5.png)\n\n**300天数据之旅第79天！**\n- 在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《使用PyTorch进行深度学习》这本书的内容。在这里，我学习了数据加载与解析、CT扫描数据集、数据流水线等相关主题。此外，我还了解了自动编码器、循环神经网络以及长短期记忆网络（LSTM）、数据处理、独热编码、训练集与验证集的随机划分等内容。我继续研究LUNA数据集，即2016年肺结节分析数据集。LUNA大型挑战赛结合了一个开放的数据集和高质量的患者CT扫描标签——其中许多包含肺结节，并且对基于该数据的分类器进行了公开排名。我在截图中展示了使用PyTorch进行简单数据准备的实现过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c117322a035f.png)\n\n**300天数据之旅第80天！**\n- 在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《使用PyTorch进行深度学习》这本书的内容。在这里，我学习了单个CT扫描数据集的加载、3D结节密度数据、SimpleITK库、亨氏单位、体素、批归一化、使用患者坐标系加载结节、毫米与体素地址之间的转换、数组坐标、矩阵乘法等相关主题。此外，我还学习了使用LSTM的自动编码器、有状态解码器模型以及数据可视化技术。我继续研究LUNA数据集，即2016年肺结节分析数据集。我在截图中展示了使用PyTorch在CT扫描数据集中实现患者坐标与数组坐标之间转换的代码。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_af96d912394f.png)\n\n**300天数据之旅第81天！**\n- **体素与结节**：体素是大家熟悉的2D像素在3D空间中的对应物。它不是表示一个面积，而是表示一个体积。由增生细胞组成的肺部组织团块称为肿瘤。而宽度只有几毫米的小型肿瘤则被称为结节。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用PyTorch进行深度学习》这本书的内容。在这里，我学习了PyTorch数据集实例的实现、LUNA数据集类、交叉熵损失、正负结节、数组与张量、候选数组缓存、训练集与验证集、数据可视化等相关主题。此外，我还学习了数据归一化、方差阈值、RDKIT库等内容。我在截图中展示了使用PyTorch准备LUNA数据集的实现过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《使用PyTorch进行深度学习》**](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Fdeep-learning-with-pytorch)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8e811f837261.png)\n\n**300天数据之旅第82天！**\n- **标签算法**：学习预测非互斥类别的问题称为多标签分类。自动标签问题通常被描述为多标签分类问题。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入理解深度学习》这本书的内容。在这里，我学习了机器学习的激励性示例、学习算法、训练过程、数据、特征、模型、目标函数、优化算法、监督学习、回归、二分类、多分类和层次分类、交叉熵与均方误差损失函数、梯度下降、标签算法等相关主题。我在截图中展示了使用PyTorch进行数据准备、归一化、去除低方差特征以及构建数据加载器的实现过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入理解深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  \n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98a3ce066966.png)\n\n**第83天，300天数据之旅！**\n- **强化学习**：强化学习描述了一类非常通用的问题，其中智能体在一系列时间步中与环境交互，接收观测并选择行动。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在此过程中，我学习了搜索算法、推荐系统、序列学习、标注与解析、机器翻译、无监督学习、与环境交互及强化学习、数据处理、数学运算、广播机制、索引与切片、张量内存优化、数据类型转换等主题。我在截图中展示了使用PyTorch实现的数学运算、张量拼接、广播机制和数据类型转换。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fd6f21a32f20.png)\n\n**第84天，300天数据之旅！**\n- **张量**：张量是指用任意维度的数组来描述的代数对象，可以有多个轴。向量是一阶张量，矩阵是二阶张量。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在此过程中，我学习了数据处理、读取数据集、处理缺失值、处理分类数据、转换为张量格式、线性代数（如标量、向量、长度、维度与形状）、矩阵、对称矩阵、张量、张量算术的基本性质、降维与非降维求和、点积、矩阵-向量乘法等主题。我在截图中展示了使用PyTorch实现的数据处理、缺失值处理、标量、向量、矩阵和点积的操作。希望你能从中获得一些见解，并进一步实践。也建议你花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f0f412a5b340.png)\n\n**第85天，300天数据之旅！**\n- **穷竭法**：古代通过在圆形等曲面图形内嵌入多边形，使这些多边形更接近圆的形状，从而计算曲面面积的方法称为穷竭法。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在此过程中，我学习了矩阵乘法、L1和L2范数归一化、Frobenius范数归一化、微积分、穷竭法、导数与微分、偏导数、梯度下降、链式法则、自动微分、非标量变量的反向传播、分离计算图、反向传播、利用控制流计算梯度等主题。我在截图中展示了使用PyTorch实现的矩阵乘法、L1、L2和Frobenius范数归一化、导数与微分、自动微分以及梯度计算。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5b1d1d6e382e.png)\n\n**第86天，300天数据之旅！**\n- **穷竭法**：古代通过在圆形等曲面图形内嵌入多边形，使这些多边形更接近圆的形状，从而计算曲面面积的方法称为穷竭法。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在此过程中，我学习了概率、基础概率论、采样、多项分布、概率论公理、随机变量、处理多个随机变量、联合概率、条件概率、贝叶斯定理、边缘化、独立性与依赖性、期望与方差、模块中的类与函数等主题。我在截图中展示了使用PyTorch实现的多项分布、概率可视化以及导数与微分操作。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5a7859144653.png)\n\n**第87天，300天数据之旅！**\n- **超参数**：那些可以在训练过程中调整但不会在训练循环中更新的参数被称为超参数。超参数调优是指选择和调整超参数的过程，通常需要根据训练结果进行反复试验。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在此过程中，我学习了线性回归、线性回归的基本要素、线性模型与变换、损失函数、解析解、小批量随机梯度下降、利用已学模型进行预测、速度向量化、正态分布与平方损失、从线性回归到深度神经网络、生物学解释、超参数调优等主题。我在截图中展示了使用Python实现的速度向量化和正态分布操作。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ef290516d277.png)\n\n**300天数据之旅第88天！**\n- **超参数**：在训练过程中可调但不更新的参数称为超参数。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了从零开始实现线性回归、数据流水线、深度学习框架、生成人工数据集、散点图与相关性、读取数据集、小批量处理、特征与标签、并行计算、初始化模型参数、小批量随机梯度下降、定义简单线性回归模型、广播机制、向量与标量等主题。我在截图中展示了使用PyTorch生成合成数据集、绘制散点图、读取数据集、初始化模型参数以及定义线性回归模型的实现过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a989772c00da.png)\n\n**300天数据之旅第89天！**\n- **线性回归**：线性回归是一种线性方法，用于建模标量响应与一个或多个解释变量（即因变量和自变量）之间的关系。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了线性回归、损失函数的定义、优化算法的定义、小批量随机梯度下降、模型训练、张量与微分、线性回归的简洁实现、生成合成数据集、模型评估等相关内容。我在截图中展示了使用PyTorch定义损失函数、执行小批量随机梯度下降、训练与评估模型、线性回归的简洁实现以及读取数据集的实现过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3b62e29647d8.png)\n\n**300天数据之旅第90天！**\n- **线性回归**：线性回归是一种线性方法，用于建模标量响应与一个或多个解释变量（即因变量和自变量）之间的关系。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了Softmax回归、分类问题、网络架构、全连接层的参数化代价、Softmax运算、小批量的向量化、损失函数、对数似然、Softmax及其导数、交叉熵损失、信息论基础、熵与惊讶度、模型预测与评估、图像分类数据集等相关内容。我在截图中展示了使用PyTorch实现图像分类数据集、可视化、Softmax回归及运算，并附带模型参数的实现过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3af372a9cf72.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_37ee46639977.png)\n\n**300天数据之旅第91天！**\n- **激活函数**：激活函数通过计算加权总和并加上偏置来决定神经元是否被激活。它们是可微分的运算符。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了交叉熵损失函数、分类准确率与训练、Softmax回归、模型参数、优化算法、多层感知机、隐藏层、线性模型的问题、从线性到非线性模型、通用逼近定理、激活函数如ReLU函数、Sigmoid函数、Tanh函数、导数与梯度等相关内容。我在截图中展示了使用PyTorch实现Softmax回归模型、分类准确率、ReLU函数、Sigmoid函数、Tanh函数，并配有可视化效果。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7b724143f6d1.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f93b1679177f.png)\n\n**300天数据之旅第92天！**\n- **激活函数**：激活函数通过计算加权总和并加上偏置来决定神经元是否被激活。它们是可微分的运算符。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了多层感知机的实现、模型参数的初始化、ReLU激活函数、交叉熵损失函数、模型训练、全连接层、简单线性层、Softmax回归及函数、随机梯度下降、Sequential API、高级API、学习率、权重与偏置、张量、超参数等相关内容。我在截图中展示了使用PyTorch实现多层感知机、ReLU激活函数、模型训练及模型评估的过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f210638c105a.png)\n\n**300天数据之旅第93天！**\n- **多层感知机**：最简单的深度神经网络称为多层感知机。它们由多层神经元组成。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了模型选择、欠拟合、过拟合、训练误差与泛化误差、统计学习理论、模型复杂度、早停法、训练集、测试集和验证集、K折交叉验证、数据集大小、多项式回归、数据集的生成、模型的训练与测试、三阶多项式函数拟合、线性函数拟合、高阶多项式函数拟合、权重衰减、归一化等主题。我在截图中展示了使用PyTorch生成数据集、定义训练函数以及进行多项式函数拟合的实现过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3658210dc39e.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e4c6e525ddf8.png)\n\n**300天数据之旅第94天！**\n- **多层感知机**：最简单的深度神经网络称为多层感知机。它们由多层神经元组成，每一层都与下一层完全连接，接收来自下层的输入，并影响上层。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了高维线性回归、模型参数、L2正则化惩罚项的定义、训练循环的定义、正则化与权重衰减、Dropout与过拟合、偏差与方差权衡、高斯分布、随机梯度下降、训练误差与测试误差等相关内容。我在截图中展示了使用PyTorch实现高维线性回归、模型参数、L2正则化惩罚项以及正则化和权重衰减的过程。希望你能从中获得一些见解，并加以实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d81addcc47f5.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dc6be0fd1e76.png)\n\n**300天数据之旅第95天！**\n- **Dropout与协同适应**：Dropout是在前向传播过程中，在计算每一层时注入噪声的过程。协同适应则是神经网络中的一种现象，表现为每一层都依赖于前一层激活的具体模式。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了Dropout、过拟合、泛化误差、偏差与方差权衡、通过扰动提高鲁棒性、L2正则化与权重衰减、协同适应、Dropout概率、Dropout层、Fashion MNIST数据集、激活函数、随机梯度下降、Sequential API与Functional API等相关内容。我在截图中展示了使用PyTorch实现Dropout层以及训练和测试模型的过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0904cdd9aae3.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_888865870138.png)\n\n**300天数据之旅第96天！**\n- **Dropout与协同适应**：Dropout是在前向传播过程中，在计算每一层时注入噪声的过程。协同适应则是神经网络中的一种现象，表现为每一层都依赖于前一层激活的具体模式。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了前向传播、反向传播与计算图、数值稳定性、梯度消失与爆炸、打破对称性、参数初始化、环境与分布偏移、协变量偏移、标签偏移、概念偏移、非平稳分布、经验风险与真实风险、批量学习、在线学习、强化学习等相关内容。我在截图中展示了使用PyTorch进行数据预处理和数据准备的实现过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n- [**预测房价**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices\u002Fblob\u002Fmain\u002FPredictingHousePrices.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2309b2ccd781.png)\n\n**300天数据之旅第97天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了深度网络的训练与构建、数据集的下载与缓存、数据预处理、回归问题、数据集的访问与读取、数值型特征与离散类别型特征、优化与方差、数组与张量、简单线性模型、Sequential API、均方根误差、Adam优化器、超参数调优、K折交叉验证、训练误差与验证误差、模型选择、过拟合与正则化等主题。我在这里通过截图展示了使用PyTorch实现的简单线性模型、均方根误差、训练函数以及K折交叉验证。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n- [预测房价](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices\u002Fblob\u002Fmain\u002FPredictingHousePrices.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_14e30ac0f165.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_29d5568a2f04.png)\n\n**300天数据之旅第98天！**\n- **常量参数**：常量参数是指既不是前一层计算结果，也不是神经网络中可更新参数的项。在我的机器学习和深度学习之旅中，今天我同样阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了K折交叉验证、训练与预测、超参数优化、深度学习计算、层与块、Softmax回归、多层感知机、ResNet架构、前向传播与反向传播函数、ReLU激活函数、Sequential块的实现、MLP的实现、常量参数等主题。我在这里通过截图展示了使用PyTorch实现的MLP、Sequential API类以及前向传播函数。希望你能从中获得一些见解，并进一步探索。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n- [预测房价](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCaliforniaHousing__Prices\u002Fblob\u002Fmain\u002FPredictingHousePrices.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_200f24416b75.png)\n\n**300天数据之旅第99天！**\n- **常量参数**：常量参数是指既不是前一层计算结果，也不是神经网络中可更新参数的项。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了参数管理、参数访问、目标参数、从嵌套块中收集参数、参数初始化、自定义初始化、共享参数、延迟初始化、多层感知机、输入维度、自定义层的定义、无参数层、前向传播函数、常量参数、Xavier初始化、权重与偏置等主题。我在这里通过截图展示了使用PyTorch实现的参数访问、参数初始化、共享参数以及无参数层。希望你能从中获得一些启发，并进一步研究。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dd29199545c7.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e7f2ef31f21c.png)\n\n**300天数据之旅第100天！**\n- **不变性和局部性原则**：平移不变性原则指出，无论同一图像区域出现在何处，网络对其的响应都应相同。局部性原则则强调，网络应专注于局部区域，而不受远处区域内容的影响。在我的机器学习和深度学习之旅中，今天我依然阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了全连接层到卷积层的转换、平移不变性、局部性原则、对MLP的约束、卷积神经网络、互相关运算、图像与通道、文件IO、张量及模型参数的加载与保存、自定义层、有参数层等主题。我在这里通过截图展示了使用PyTorch实现的有参数层、张量及模型参数的加载与保存。希望你能从中获得一些启发，并进一步探索。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_86e3ac6cef51.png)\n\n**300天数据之旅第101天！**\n- **不变性和局部性原则**：平移不变性原则指出，无论同一图像区域出现在何处，网络对其的响应都应相同。局部性原则则强调，网络应专注于局部区域，而不受远处区域内容的影响。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了卷积神经网络、用于图像的卷积、互相关运算、卷积层、构造函数与前向传播函数、权重与偏置、图像中的物体边缘检测、卷积核的学习、反向传播、特征图与感受野、卷积核参数等主题。我在这里通过截图展示了使用PyTorch实现的互相关运算、卷积层以及卷积核的学习。希望你能从中获得一些启发，并进一步研究。也希望大家能花些时间学习上述书籍及其他相关资料中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9bb441ec8b94.png)\n\n**第102天，300天数据之旅！**\n- **最大池化**：池化操作由一个固定形状的窗口组成，该窗口按照步幅在输入的所有区域上滑动，为每个位置计算出一个输出值，这个输出值可以是池化窗口内元素的最大值或平均值。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了填充与步幅、步幅卷积、互相关、多输入多输出通道、卷积层、最大池化层和平均池化层、池化窗口与操作、卷积神经网络、LeNet架构、监督学习、卷积编码器、Sigmoid激活函数以及与此相关的其他主题。我在这里通过快照展示了使用PyTorch实现的卷积神经网络、填充、步幅和池化层，以及多通道的实现。希望你能从中获得一些启发，并进一步深入研究。我也希望你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d21a083ab81f.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_06e66bfd0ee4.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f054e0a19483.png)\n\n**第103天，300天数据之旅！**\n- **VGG网络**：VGG网络通过可重用的卷积块构建网络。VGG模型由每个卷积块中的卷积层数量和输出通道数来定义。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了卷积神经网络、监督学习、深度CNN和AlexNet、支持向量机与特征、表示学习、数据与硬件加速器问题、LeNet和AlexNet的架构、ReLU等激活函数、使用CNN块的网络、VGG神经网络架构、填充与池化、卷积层、Dropout、全连接层和线性层，以及与此相关的其他主题。我在这里通过快照展示了使用PyTorch实现的AlexNet架构和VGG网络架构，以及CNN块的应用。希望你能从中获得一些启发，并进一步深入研究。我也希望你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3cce212c56e6.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_50dc1b56d5cb.png)\n\n**第104天，300天数据之旅！**\n- **VGG网络**：VGG网络通过可重用的卷积块构建网络。VGG模型由每个卷积块中的卷积层数量和输出通道数来定义。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了网络中的网络（NIN）架构、NIN块与模型、卷积层、ReLU激活函数、顺序API与函数式API、全局平均池化层、具有并行拼接的网络（GoogLeNet）、Inception块、GoogLeNet模型与架构、最大池化层、模型训练，以及与此相关的其他主题。我在这里通过快照展示了使用PyTorch实现的NIN块与模型、Inception块以及GoogLeNet模型。希望你能从中获得一些启发，并进一步深入研究。我也希望你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_250eba7117f5.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_16df9904ab51.png)\n\n**第105天，300天数据之旅！**\n- **批量归一化**：批量归一化通过利用小批量的均值和标准差，持续调整神经网络的中间输出，从而使中间输出的值更加稳定。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了批量归一化、深度神经网络的训练、尺度参数与偏移参数、批量归一化层、全连接层、卷积层、预测时的批量归一化、张量、均值与方差、在LeNet中应用BN、使用高级API进行BN的简洁实现、内部协变量偏移、Dropout层、残差网络（ResNet）、函数类、残差块等与此相关的其他主题。我在这里通过快照展示了使用PyTorch实现的批量归一化架构。希望你能从中获得一些启发，并进一步深入研究。我也希望你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_788cdd325401.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e2298dbf9123.png)\n\n**第106天，300天数据之旅！**\n- **批量归一化**：批量归一化通过利用小批量的均值和标准差，持续调整神经网络的中间输出，从而使中间输出的值更加稳定。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了密集连接神经网络（DenseNet）、密集块、批量归一化、激活函数与卷积层、过渡层、残差网络（ResNet）、函数类、残差块、残差映射、残差连接、ResNet模型、最大和平均池化层、模型训练，以及与此相关的其他主题。我在这里通过快照展示了使用PyTorch实现的ResNet架构和ResNet模型。希望你能从中获得一些启发，并进一步深入研究。我也希望你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f11ccc27237f.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_42987ac07b27.png)\n\n**300天数据之旅第107天！**\n- **序列模型**：在已知观测值之外进行的预测称为外推。而在现有观测值之间进行的估计则称为插值。序列模型需要专门的统计工具来进行估计，比如自回归模型。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了DenseNet模型、卷积层、循环神经网络、序列模型、插值与外推、统计工具、自回归模型、潜在自回归模型、马尔可夫模型、强化学习算法、因果关系、条件概率分布、训练多层感知机、单步预测等与此相关的主题。我在截图中展示了使用PyTorch实现的DenseNet架构以及简单的RNN实现。希望你能从中获得一些启发，并进一步深入研究。也期待你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bfd8a19caee5.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eb08fc134112.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_618be2eee6a8.png)\n\n**300天数据之旅第108天！**\n- **分词与词汇表**：分词是将字符串或文本拆分为一系列标记的过程。词汇表则是将字符串标记映射为数字索引的字典。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了文本预处理、文本语料库、分词函数、序列模型与数据集、词汇表、字典、多层感知机、单步预测、多步预测、张量、循环神经网络等与此相关的主题。我在截图中展示了使用PyTorch读取数据集、进行分词和构建词汇表的实现过程。希望你能从中获得一些见解，并继续深入探索。也期待你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_81feff0ca6c7.png)\n\n**300天数据之旅第109天！**\n- **顺序划分**：顺序划分是一种在遍历小批量时保持分割子序列顺序的策略。它确保在迭代过程中，两个相邻小批量中的子序列在原始序列中也是相邻的。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了语言模型与序列数据集、条件概率、拉普拉斯平滑、马尔可夫模型与N元模型、一元模型、二元模型和三元模型、自然语言统计、停用词、词频、齐普夫定律、长序列数据的读取、小批量、随机采样、顺序划分等与此相关的主题。我在截图中展示了使用PyTorch实现的一元、二元和三元模型词频、随机采样以及顺序划分的过程。希望你能从中获得一些启发，并继续深入研究。也期待你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2894820ea223.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_81b2ba3e6297.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_03cc7cf9636d.png)\n\n**300天数据之旅第110天！**\n- **循环神经网络**：循环神经网络是一种利用递归计算来更新隐藏状态的网络结构。RNN的隐藏状态可以捕捉到当前时间步之前整个序列的历史信息。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了循环神经网络（RNN）、隐藏状态、无隐藏状态的神经网络、有隐藏状态的神经网络、RNN层、基于RNN的字符级语言模型、困惑度、从零开始实现RNN、独热编码、词汇表、模型参数初始化、RNN模型、小批量和双曲正切激活函数、预测与预热期、梯度裁剪、反向传播等与此相关的主题。我在截图中展示了使用PyTorch实现的RNN模型、梯度裁剪以及模型训练的过程。希望你能从中获得一些启发，并继续深入研究。也期待你能花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cd54a74d878e.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5b69b1b64e0b.png)\n\n**300天数据之旅第111天！**\n- **循环神经网络**：循环神经网络是一种通过递归计算隐藏状态的网络。RNN的隐藏状态可以捕捉序列到当前时间步的历史信息。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了循环神经网络的实现、RNN模型的定义、训练与预测、随时间反向传播、梯度爆炸、梯度消失、RNN中梯度的分析、完整计算、截断时间步、随机截断、RNN中的梯度计算策略、激活函数、常规截断等主题。我在截图中展示了使用PyTorch实现的循环神经网络、训练与预测过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e065fcfce3eb.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5ad0e694efb3.png)\n\n**300天数据之旅第112天！**\n- **门控循环单元**：门控循环单元（GRU）是循环神经网络中的一种门控机制，用于决定何时更新隐藏状态以及何时重置隐藏状态。它旨在解决标准RNN中存在的梯度消失问题。在我的机器学习和深度学习之旅中，今天我同样阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了现代循环神经网络、梯度裁剪、门控循环单元（GRU）、记忆细胞、门控隐藏状态、重置门与更新门、广播操作、候选隐藏状态、哈达玛积运算符、隐藏状态、模型参数初始化、GRU模型的定义、训练与预测等相关主题。我在截图中展示了使用PyTorch实现的门控循环单元、GRU模型、训练与预测的过程。希望你能从中获得一些见解，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_92484e8c5773.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fa1a700bbed0.png)\n\n**300天数据之旅第113天！**\n- **长短期记忆网络**：长短期记忆网络（LSTM）是循环神经网络的一种，能够学习序列预测问题中的顺序依赖关系。LSTM具有输入门、遗忘门和输出门，用于控制信息的流动。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了长短期记忆网络（LSTM）、门控记忆细胞、输入门、遗忘门和输出门、候选记忆细胞、双曲正切激活函数、sigmoid激活函数、记忆细胞、隐藏状态、模型参数初始化、LSTM模型的定义、训练与预测、门控循环单元（GRU）、高斯分布等相关主题。我在截图中展示了使用PyTorch实现的长短期记忆网络模型、训练与预测过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1cb795c7d392.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_753817bcb99c.png)\n\n**300天数据之旅第114天！**\n- **长短期记忆网络**：长短期记忆网络（LSTM）是循环神经网络的一种，能够学习序列预测问题中的顺序依赖关系。LSTM具有输入门、遗忘门和输出门，用于控制信息的流动。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了深层循环神经网络、函数依赖关系、双向循环神经网络、隐马尔可夫模型中的动态规划、双向模型、计算成本与应用、机器翻译与数据集、数据集预处理、分词、词汇表、文本序列填充等相关主题。我在截图中展示了使用PyTorch实现的数据集下载、预处理、分词及构建词汇表的过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8b6b7c67afc9.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2549138c4c81.png)\n\n**300天数据之旅第115天！**\n- **编码器-解码器架构**：编码器接收变长序列作为输入，并将其转换为固定形状的状态。解码器则将这种固定形状的编码状态映射回变长序列。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了编码器与解码器架构、机器翻译模型、序列转导模型、前向传播函数、序列到序列学习、循环神经网络、嵌入层、门控循环单元（GRU）层、隐藏状态与单元、RNN编码器-解码器架构、词汇表等相关主题。我在截图中展示了使用PyTorch实现的编码器、解码器架构以及用于序列到序列学习的RNN编码器-解码器模型。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_95be6b759068.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_585f04323f11.png)\n\n**300天数据之旅第116天！**\n- **序列搜索**：贪婪搜索是基于输入序列生成输出序列的条件概率。束搜索是贪婪搜索的一种改进版本，引入了一个名为“束宽”的超参数。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了Softmax交叉熵损失函数、序列掩码、教师强制、训练与预测、预测序列的评估、BLEU（双语评估替代）指标、RNN编码器-解码器模型、束搜索、贪婪搜索、穷举搜索、注意力机制、注意力线索、非自主性线索与自主性线索、查询、键与值、注意力池化等主题。我在截图中展示了使用PyTorch实现的序列掩码、Softmax交叉熵损失、RNN编码器-解码器模型的训练以及BLEU的计算过程。希望你能从中获得一些启发，并进一步深入研究。也期待你能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_24d936866d99.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f433bd706d3d.png)\n\n**300天数据之旅第117天！**\n- **注意力池化**：注意力池化会根据权重选择性地聚合数值或感官输入，从而生成输出。它体现了查询与键之间的交互作用。在我的机器学习和深度学习之旅中，今天我同样阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了注意力池化，即纳达拉亚-沃森核回归、查询（自主性线索）与键（非自主性线索）、数据集的生成、平均池化、非参数化注意力池化、注意力权重、高斯核、参数化注意力池化、批量矩阵乘法、模型定义、模型训练、随机梯度下降、MSE损失函数等主题。我在截图中展示了使用PyTorch实现的注意力机制、非参数化注意力池化、批量矩阵乘法、NW核回归模型、训练与预测等内容。希望你能从中获得一些见解，并继续探索相关领域。也希望你能抽出时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e67983c73ff8.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98615d2603b7.png)\n\n**300天数据之旅第118天！**\n- **注意力池化**：注意力池化会根据权重选择性地聚合数值或感官输入，从而生成输出。它体现了查询（自主性线索）与键（非自主性线索）之间的交互作用。注意力池化实际上是训练输出的加权平均。它可以是参数化的，也可以是非参数化的。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了注意力评分函数、高斯核、注意力权重、Softmax激活函数、掩码Softmax操作、文本序列、概率分布、加性注意力、查询、键与值、Tanh激活函数、Dropout与线性层、注意力池化等主题。我在截图中展示了使用PyTorch实现的掩码Softmax操作和加性注意力。希望你能从中获得一些启发，并进一步研究这些内容。也期待你能花些时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c7e03d9859e2.png)\n\n**300天数据之旅第119天！**\n- **注意力池化**：注意力池化会根据权重选择性地聚合数值或感官输入，从而生成输出。它体现了查询（自主性线索）与键（非自主性线索）之间的交互作用。注意力池化是训练输出的加权平均，可以是参数化的，也可以是非参数化的。在我的机器学习和深度学习之旅中，今天我依然阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了缩放点积注意力、查询、键与值、加性注意力、注意力池化、Bahdanau注意力、RNN编码器-解码器架构、隐藏状态、嵌入、带注意力的解码器定义、序列到序列注意力解码器等主题。我在截图中展示了使用PyTorch实现的缩放点积注意力和序列到序列注意力解码器模型。希望你能从中获得一些启发，并继续深入研究。也期待你能花些时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5132a646704c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d682cc6b9db7.png)\n\n**300天数据之旅第120天！**\n- **多头注意力机制**：多头注意力机制是一种注意力机制的设计，它通过并行运行多次注意力机制来实现。与只进行一次注意力池化不同，查询、键和值可以被转换为学习到的线性投影，然后并行输入到注意力池化中。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了Bahdanau注意力机制、循环神经网络编码器-解码器架构、序列到序列模型的训练、嵌入层、注意力权重、GRU、热力图、多头注意力机制、查询、键和值、注意力池化、加性注意力和缩放点积注意力、转置函数等主题。我在截图中展示了使用PyTorch实现的多头注意力机制。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ed65da7ab752.png)\n\n**300天数据之旅第121天！**\n- **多头注意力机制**：多头注意力机制是一种注意力机制的设计，它通过并行运行多次注意力机制来实现。与只进行一次注意力池化不同，查询、键和值可以被转换为学习到的线性投影，然后并行输入到注意力池化中。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了多头注意力机制、查询、键和值、注意力池化、缩放点积注意力、自注意力和位置编码、循环神经网络、内部注意力机制、CNN、RNN和自注意力的比较、填充标记、绝对位置信息、相对位置信息等主题。我在截图中展示了使用PyTorch实现的位置编码。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_52c39b78635c.png)\n\n**300天数据之旅第122天！**\n- **Transformer架构**：Transformer是一种利用编码器和解码器两部分将一个序列转换为另一个序列的架构，它使用了自注意力机制。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了Transformer、自注意力、编码器和解码器架构、序列嵌入、位置编码、逐位置前馈网络、残差连接和层归一化、编码器模块和多头自注意力、Transformer解码器、查询、键和值、缩放点积注意力等主题。我在截图中展示了使用PyTorch实现的逐位置前馈网络、残差连接和层归一化、编码器、解码器模块以及Transformer解码器。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_750baaf29402.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_95af79810722.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b7f653ab6266.png)\n\n**300天数据之旅第123天！**\n- **Transformer架构**：Transformer是一种利用编码器和解码器两部分将一个序列转换为另一个序列的架构，它使用了自注意力机制。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了解码器架构、自注意力、编码器-解码器注意力、逐位置前馈网络、残差连接、Transformer解码器、嵌入层、序列块、Transformer架构的训练等主题。我还学习了逻辑回归、Sigmoid激活函数、权重初始化、梯度下降、损失函数等内容。我在截图中展示了使用NumPy从零开始实现的逻辑回归、使用PyTorch实现的Transformer解码器以及训练过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《逻辑回归文档》](https:\u002F\u002Fml-cheatsheet.readthedocs.io\u002Fen\u002Flatest\u002Flogistic_regression.html)\n  - [《逻辑回归实现》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Ftree\u002Fmain\u002FLogisticRegression)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_30d0eb86e30c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eff4d2cccee8.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_963bbbeb8b7d.png)\n\n**300天数据之旅第124天！**\n- **Transformer架构**：Transformer是一种通过编码器和解码器两部分将一个序列转换为另一个序列的架构，它利用了自注意力机制。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了优化算法与深度学习、目标函数与最小化、优化的目标、泛化误差、训练误差、风险函数与经验风险函数、优化挑战、局部极小值与全局极小值、鞍点、海森矩阵与特征值、梯度消失、凸性、凸集与凸函数、Jensen不等式以及与此相关的其他主题。我在这里用PyTorch实现了局部极小值、鞍点、梯度消失和凸函数的相关内容，并附上了截图。希望你能从中获得一些启发，并进一步深入研究。同时，也建议你花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_07e8730fcf37.png)\n\n**300天数据之旅第125天！**\n- **梯度下降法**：梯度下降法是一种优化算法，通过沿着负梯度方向迭代更新参数来最小化可微函数。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了凸性和二阶导数、约束优化、拉格朗日函数与乘子、惩罚项、投影、梯度裁剪、随机梯度下降法、一维梯度下降法、目标函数、学习率、局部极小值与全局极小值、多元梯度下降法以及其他相关主题。我在这里用PyTorch实现了一维梯度下降法、局部极小值和多元梯度下降法，并附上了截图。希望你能从中获得一些见解，并加以实践。同时，也建议你花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4b88587d5b0d.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_44cecff1cc78.png)\n\n**300天数据之旅第126天！**\n- **梯度下降法**：梯度下降法是一种优化算法，通过沿着负梯度方向迭代更新参数来最小化可微函数。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了多元梯度下降法、自适应方法、学习率、牛顿法、泰勒展开、海森矩阵、梯度与反向传播、非凸函数、收敛性分析、线性收敛、预处理、带线搜索的梯度下降法、随机梯度下降法、损失函数以及其他相关主题。我在这里用PyTorch实现了牛顿法、非凸函数和随机梯度下降法，并附上了截图。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0fe15c6f0f8b.png)\n\n**300天数据之旅第127天！**\n- **随机梯度下降法**：随机梯度下降法是一种用于优化具有适当可微性质的目标函数的迭代方法，它是梯度下降法的一种变体，通过计算误差并更新模型来进行优化。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了随机梯度下降法、动态学习率、指数衰减与多项式衰减、凸目标的收敛性分析、随机梯度与有限样本、小批量随机梯度下降法、向量化与缓存、矩阵乘法、小批量、方差、梯度实现等相关主题。我在这里用PyTorch实现了随机梯度下降法和小批量随机梯度下降法，并附上了截图。希望你能从中获得一些见解，并加以实践。同时，也建议你花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5226ba7bd254.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6c532f246d65.png)\n\n**300天数据之旅第128天！**\n- **随机梯度下降法**：随机梯度下降法是一种用于优化具有适当可微性质的目标函数的迭代方法，它是梯度下降法的一种变体，通过计算误差并更新模型来进行优化。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了动量法、随机梯度下降法、滑动平均、方差、加速梯度、病态问题与收敛性、有效样本权重、实际实验、动量法与SGD的结合实现、理论分析、二次凸函数、标量函数以及其他相关主题。我在这里用PyTorch实现了动量法、有效样本权重和标量函数的相关内容，并附上了截图。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习上述书籍及其他相关资料。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2b73356e7225.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fda22f3cfd2c.png)\n\n**300天数据之旅第129天！**\n- **随机梯度下降**：随机梯度下降是一种用于优化具有适当可微性质的目标函数的迭代方法。它是梯度下降算法的一种变体，通过计算误差并更新模型来实现优化。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了Adagrad优化算法、稀疏特征与学习率、预处理、随机梯度下降算法、相关算法、从零开始实现Adagrad、深度学习与计算约束、学习率等主题。我在截图中展示了使用PyTorch从零开始实现Adagrad优化算法的过程。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c0fb6dcb9366.png)\n\n**300天数据之旅第130天！**\n- **RMSProp优化算法**：RMSProp是一种基于梯度的优化算法，它利用近期梯度的大小来对梯度进行归一化处理。该算法解决了Adagrad学习率急剧衰减的问题，通过将学习率除以梯度平方的指数加权移动平均值来进行调整。在我的机器学习和深度学习之旅中，今天我同样阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了RMSProp优化算法、学习率、漏斗式平均与动量法、从零开始实现RMSProp、梯度下降算法、预处理等相关主题。我在截图中展示了使用PyTorch从零开始实现RMSProp优化算法的过程。希望你能从中获得一些见解，并进一步探索。也希望大家能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3cd7ad5baf1b.png)\n\n**300天数据之旅第131天！**\n- **RMSProp优化算法**：RMSProp是一种基于梯度的优化算法，它利用近期梯度的大小来对梯度进行归一化处理。该算法解决了Adagrad学习率急剧衰减的问题，通过将学习率除以梯度平方的指数加权移动平均值来进行调整。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了Adadelta优化算法、学习率、漏斗式平均、动量、梯度下降、Adadelta的简洁实现、Adam优化算法、向量化与小批量随机梯度下降、权重参数、归一化、Adam算法的简洁实现等相关主题。我在截图中展示了使用PyTorch从零开始实现Adadelta优化算法和Adam优化算法的过程。希望你也能够花些时间学习上述书籍中提到的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f28ebc8cc725.png)\n\n**300天数据之旅第132天！**\n- **Adam优化器**：Adam使用指数加权移动平均（也称为漏斗式平均）来同时估计动量和梯度的二阶矩。它结合了多种优化算法的优点，在小批量随机梯度下降的基础上应用EWMA。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了Adam和Yogi优化算法、方差、小批量随机梯度下降、学习率调度、权重向量、卷积层、全连接层、最大池化层、Sequential API、ReLU、交叉熵损失、调度器、过拟合等相关主题。我在截图中展示了使用PyTorch实现LeNet架构和Yogi优化算法的过程。希望你能从中获得一些启发，并进一步研究。也希望大家能花些时间学习上述书籍中提到的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b7a6244a2a4c.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_68172c2da316.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fb1aeb9839c9.png)\n\n**300天数据之旅第133天！**\n- **Adam优化器**：Adam使用指数加权移动平均（也称为漏斗式平均）来同时估计动量和梯度的二阶矩。它结合了多种优化算法的优点，在小批量随机梯度下降的基础上应用EWMA。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了学习率调度、平方根调度、因子调度、学习率与多项式衰减、多因子调度、分段常数调度、优化与局部最小值、余弦调度等相关主题。我在截图中展示了使用PyTorch实现多因子调度和余弦调度的过程。希望你能从中获得一些见解，并进一步探索。也希望大家能花些时间学习上述书籍中提到的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_71e32b2b7c15.png)\n\n**第134天，300天数据之旅！**\n- **Adam优化器**：Adam使用指数加权移动平均（也称为漏桶平均）来估计梯度的动量和二阶矩。它结合了多种优化算法的优点，在小批量随机梯度下降的基础上应用EWMA。在我的机器学习和深度学习旅程中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了模型计算性能、编译器与解释器、符号式编程与命令式编程、混合编程、动态计算图、混合顺序执行、通过混合加速、多层感知机、异步计算等主题。我在这里的截图中展示了使用PyTorch实现的混合顺序执行、通过混合加速以及异步计算。希望你能从中获得一些启发，并进一步实践。我也希望你能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e007f1ad8fe8.png)\n\n**第135天，300天数据之旅！**\n- **Adam优化器**：Adam使用指数加权移动平均（也称为漏桶平均）来估计梯度的动量和二阶矩。它结合了多种优化算法的特点，在小批量随机梯度下降的基础上应用EWMA。在我的机器学习和深度学习旅程中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了异步计算、屏障与阻塞机制、优化计算与内存占用、自动并行化、并行计算与通信、多GPU训练、问题分解、数据并行、网络划分、逐层划分、数据并行划分等主题。我在这里的截图中展示了使用PyTorch初始化模型参数和定义LeNet模型的实现。目前我仍在继续完成LeNet模型的实现。希望你能从中获得一些见解，并加以实践。我也希望你能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**LeNet架构的实现**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d48cfb755ebe.png)\n\n**第136天，300天数据之旅！**\n- **Adam优化器**：Adam使用指数加权移动平均（也称为漏桶平均）来估计梯度的动量和二阶矩。它结合了多种优化算法的特点，在小批量随机梯度下降的基础上应用EWMA。在我的机器学习和深度学习旅程中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了多GPU训练、LeNet架构、数据同步、模型并行、数据广播、数据分发、优化算法、反向传播实现、模型动画、交叉熵损失函数、卷积层、ReLU激活函数、矩阵乘法、平均池化层等主题。我在这里的截图中展示了使用PyTorch实现的数据分发、数据同步以及训练函数。希望你能从中获得一些启发，并加以实践。我也希望你能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**LeNet架构的实现**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3bc22fda0e6f.png)\n\n**第137天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了优化与同步、ResNet神经网络架构、卷积层、批归一化层、步幅与填充、Sequential API、参数初始化与逻辑、小批量梯度下降、ResNet模型训练、随机梯度下降优化器、交叉熵损失函数、反向传播、并行化等主题。我在这里的截图中展示了使用PyTorch实现的ResNet架构、模型初始化及训练过程。希望你能从中获得一些启发，并加以实践。我也希望你能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**LeNet架构的实现**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_82dd449fe949.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fa8de5903641.png)\n\n**第138天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《动手学深度学习》一书的内容。在这里，我学习了计算机视觉应用、图像增强、深度神经网络、常见的图像增强方法如翻转和裁剪、水平翻转与垂直翻转、改变图像颜色、叠加多种图像增强方法、CIFAR10数据集、Torch Vision模块以及随机色彩抖动实例等主题。我在这里的截图中展示了使用PyTorch实现的图像翻转、裁剪以及改变图像颜色的操作。希望你能从中获得一些启发，并加以实践。我也希望你能花些时间学习上述书籍中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《动手学深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**LeNet架构的实现**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FMachineLearning__Algorithms\u002Fblob\u002Fmain\u002FLeNetArchitecture\u002FLeNetArchitecture.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b3e1876600c6.png)\n\n**300天数据之旅第139天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了图像增强、CIFAR10数据集、多GPU训练模型、模型微调、过拟合、预训练神经网络、目标初始化、ResNet模型、ImageNet数据集、RGB图像的归一化、均值与标准差、Torch Vision模块、图像翻转与裁剪、Adam优化算法、交叉熵损失函数等主题。我在截图中展示了使用PyTorch进行图像增强和图像归一化训练模型的实现。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_54ea74e4d342.png)\n\n**300天数据之旅第140天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了模型微调、预训练神经网络、图像归一化、均值与标准差、模型的定义与初始化、交叉熵损失函数、DataLoader类、学习率与随机梯度下降、模型参数、迁移学习、源模型与目标模型、权重与偏置等主题。我在截图中展示了使用PyTorch进行图像归一化、图像翻转与裁剪以及训练预训练模型的实现。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cac521236aa2.png)\n\n**300天数据之旅第141天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了目标检测与目标识别、图像分类与计算机视觉、图像与边界框、目标位置与坐标轴等主题。此外，我还花了一些时间阅读《语音与语言处理》一书。在这里，我学习了正则表达式、或运算、分组与优先级、精确率与召回率、替换与捕获组、前瞻断言、词汇、语料库等内容。我在截图中展示了使用PyTorch进行目标检测与边界框的简单实现。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**《语音与语言处理》**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_16836a07f8af.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bd1e55bb248d.png)\n\n**300天数据之旅第142天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了计算机视觉、锚框、目标检测算法、边界框、生成多个锚框、计算复杂度、尺寸与比例等主题。此外，我还花了一些时间阅读《语音与语言处理》一书。在这里，我学习了文本归一化、用于粗粒度分词与归一化的Unix工具、单词分词、命名实体识别、Penn Treebank分词等内容。我在截图中展示了使用PyTorch生成锚框、目标检测与边界框的实现。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**《语音与语言处理》**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_976037278080.png)\n\n**300天数据之旅第143天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了计算机视觉、生成多个锚框、批量大小、坐标值、交并比算法、Jaccard指数、计算复杂度、尺寸与比例等主题。此外，我还花了一些时间阅读《语音与语言处理》一书。在这里，我学习了用于分词的字节对编码算法、子词标记、WordPiece与贪婪分词算法、最大匹配算法、单词归一化、词形还原与词干提取、Porter词干提取器等内容。我在截图中展示了使用PyTorch生成锚框和交并比算法的实现。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习上述书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [**《深入浅出深度学习》**](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [**《语音与语言处理》**](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_952306388867.png)\n\n**第144天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了计算机视觉、训练集锚框的标注、目标检测与图像识别、真实框索引、锚框与偏移框、交并比及Jaccard算法等相关内容。此外，我还花了一些时间阅读《语音与语言处理》一书。在这本书中，我学习了句子分割、最小编辑距离算法、维特比算法、N元语言模型、概率、拼写纠正与语法错误纠正等内容。我在截图中展示了使用PyTorch实现训练集锚框标注和初始化偏移框的过程。希望你能从中获得一些启发，并进一步实践。也希望大家能抽出时间学习上述两本书中的相关主题。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ba8c09d28480.png)\n\n**第145天，300天数据之旅！**\n- **图像分割**：图像分割是将数字图像划分为多个区域或像素集合的过程。其目标是将图像简化为更易分析且有意义的形式。在我的机器学习和深度学习旅程中，今天我同样阅读并实践了《深入浅出深度学习》一书。在这里，我学习了非极大值抑制算法、预测边界框、真实边界框、置信度、批量大小、交并比算法或Jaccard指数、宽高比、预测用边界框、多盒目标函数、锚框等相关内容。我在截图中展示了使用PyTorch初始化多盒锚框和预测边界框的实现过程。希望你能从中获得一些见解，并加以实践。也希望大家能抽出时间学习上述两本书中的相关主题。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_25ff6bf79807.png)\n\n**第146天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了多尺度目标检测、生成多个锚框、目标检测、单次多框检测算法、类别预测层、边界框预测层、多尺度预测结果的拼接、高宽下采样模块、卷积神经网络层、ReLU与最大池化层等相关内容。此外，我还花了一段时间阅读《语音与语言处理》一书。在这里，我了解了词性标注、信息抽取、命名实体识别、正则表达式等内容。我在截图中展示了使用PyTorch初始化类别预测层和高宽下采样模块的实现过程。希望你能从中获得一些启发，并继续深入研究。也希望大家能抽出时间学习上述两本书中的相关主题。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_22899a81c6ca.png)\n\n**第147天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了单次多框检测算法、基础神经网络、高宽下采样模块、类别预测层、边界框预测层、多尺度特征块、Sequential API等内容。此外，我还花了一些时间阅读《语音与语言处理》一书。在这里，我学习了N元语言模型、概率链式法则、马尔可夫模型、最大似然估计、相对频率、语言模型评估、对数概率、困惑度、泛化与零值、稀疏性等内容。我在截图中展示了使用PyTorch实现基础SSD网络和完整SSD模型的过程。希望你能从中获得一些启发，并继续探索。也希望大家能抽出时间学习上述两本书中的相关主题。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_28416e7caffc.png)\n\n**第148天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了单次多框检测模型、Tiny SSD模型的实现、前向传播函数、数据读取与初始化、目标检测、多尺度特征块、全局最大池化层等内容。此外，我还花了一些时间阅读《语音与语言处理》一书。在这里，我了解了未知词或词汇表外单词、OOV率、平滑处理、拉普拉斯平滑、文本分类、加一平滑、最大似然估计、加K平滑等内容。我在截图中展示了使用PyTorch实现单次多框检测模型和数据集初始化的过程。希望你能从中获得一些启发，并继续深入研究。也希望大家能抽出时间学习上述两本书中的相关主题。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0cf4519cb91f.png)\n\n**第149天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了Softmax激活函数、卷积层、单次多框检测模型的训练、多尺度锚框、交叉熵损失函数、L1正则化损失函数、平均绝对误差、准确率、类别损失与偏移损失等主题。此外，我还花了一些时间阅读《语音与语言处理》一书，在这里我了解了回退与插值、Katz回退、Kneser Ney平滑、绝对折扣法、网络与愚蠢回退、困惑度与熵的关系等内容。我在快照中展示了使用PyTorch实现的单次多框检测模型训练、损失函数及评估函数。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习上述两本书中的相关主题。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - 《语音与语言处理》[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cc5ac2fe4b31.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eee396d2e433.png)\n\n**第150天，300天数据之旅！**\n- **图像分割**：图像分割是将数字图像划分为多个区域或像素集合的过程。其目标是将图像简化为更易于分析且具有实际意义的形式。在我的机器学习和深度学习之旅中，今天我同样阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了基于区域的卷积神经网络、Fast R-CNN、Faster R-CNN、Mask R-CNN、类别预测层、边界框预测层、支持向量机、RoI池化层与RoI对齐层、像素级语义、图像分割与实例分割、Pascal VOC2012语义分割、RGB通道、数据预处理等相关内容。我在快照中展示了使用PyTorch实现的语义分割及数据预处理过程。希望你能从中获得一些见解，并进一步实践。同时也建议你花些时间学习上述两本书中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_134cabc34fa6.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9a7199ea0d29.png)\n\n**第151天，300天数据之旅！**\n- **序列到序列模型**：序列到序列神经网络可以采用模块化且可重用的编码器-解码器架构构建。编码器模型会生成一个“思想向量”，即对输入数据进行密集且固定维度表示的向量。而解码器模型则利用这些思想向量来生成输出序列。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了用于自定义语义分割的数据集类、RGB通道、图像归一化、随机裁剪操作、序列到序列循环神经网络、标签编码器、独热编码器、编码与向量化、长短期记忆网络（LSTM）等相关内容。我在快照中展示了使用PyTorch实现的自定义语义分割数据集类。希望你能从中获得一些启发，并加以实践。同时也建议你花些时间学习上述两本书中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8f6f99e3986c.png)\n\n**第152天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了转置卷积层、卷积神经网络、基本的二维转置卷积、矩阵广播、卷积核大小、填充、步幅与通道数、与矩阵转置的类比关系、矩阵乘法与矩阵向量乘法等相关内容。此外，我还花了一些时间阅读《语音与语言处理》一书，在这里我了解了朴素贝叶斯与情感分类、文本分类、垃圾邮件检测、概率分类器、多项式朴素贝叶斯分类器、词袋模型、多层感知器、未知词与停用词等内容。我在快照中展示了使用PyTorch实现的转置卷积、填充、步幅以及矩阵乘法等内容。希望你能从中获得一些启发，并加以实践。同时也建议你花些时间学习上述两本书中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - 《语音与语言处理》[链接](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6ba92a7f2b98.png)\n\n**第153天，300天数据之旅！**\n- **转置卷积**：转置卷积的特点在于，其步幅和填充并不像标准卷积那样对应于在输入图像周围添加的零的数量以及卷积核滑动时的位移量。在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了全卷积神经网络、语义分割原理、转置卷积层、如何构建预训练神经网络模型、全局平均池化层、展平层、图像处理与上采样、双线性插值核函数等相关内容。我在快照中展示了使用PyTorch实现的全卷积层、预训练神经网络、双线性插值核函数以及转置卷积层的过程。希望你能从中获得一些见解，并进一步实践。同时也建议你花些时间学习上述两本书中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_86cf46eade47.png)\n\n**300天数据之旅第154天！**\n- **神经风格迁移算法**：其任务是将一个领域的图像风格转换为另一个领域的图像风格。它通过操纵图像或视频，使其呈现出另一幅图像的外观。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在此过程中，我学习了 Softmax 交叉熵损失函数、随机梯度下降法、卷积神经网络、神经网络风格迁移、合成图像、RGB 通道、归一化等与之相关的多个主题。此外，我还花了一些时间阅读《语音与语言处理》一书。书中介绍了如何优化朴素贝叶斯用于情感分析、情感词典、朴素贝叶斯作为语言模型、精确率、召回率和 F1 分数、多标签及多项式分类等内容。目前，我已开始使用神经网络进行风格迁移的工作，相关笔记本如下所示，但我仍在继续完善。\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《语音与语言处理》](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [《神经网络风格迁移》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3cb87f021996.png)\n\n**300天数据之旅第155天！**\n- **神经风格迁移算法**：其任务是将一个领域的图像风格转换为另一个领域的图像风格。它通过操纵图像或视频，使其呈现出另一幅图像的外观。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在此过程中，我学习了神经网络风格迁移、卷积神经网络、内容图像与风格图像的读取、图像的预处理与后处理、图像特征提取、合成图像、VGG 神经网络、均方误差损失函数、总变差损失函数、图像 RGB 通道的归一化等与之相关的多个主题。目前，我仍在利用神经网络进行风格迁移的工作，相关笔记本如下所示，但我仍在继续推进。我在截图中展示了使用 PyTorch 实现的特征提取函数和均方误差损失函数的代码，希望你能从中获得一些启发，并尝试实践。同时，也期待你能够花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《语音与语言处理》](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [《神经网络风格迁移》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6aeef59b0e44.png)\n\n**300天数据之旅第156天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在此过程中，我学习了合成图像的创建与初始化、同步函数、Adam 优化器、Gram 矩阵、卷积神经网络、神经网络风格迁移、损失函数等与之相关的多个主题。此外，我还花了一些时间阅读《语音与语言处理》一书。书中介绍了测试集与交叉验证、统计显著性检验、朴素贝叶斯分类器、自助法、逻辑回归、生成式与判别式分类器、特征表示、Sigmoid 分类、权重与偏置项等与之相关的多个主题。目前，我已经完成了使用神经网络进行风格迁移的工作。相关笔记本如下所示，但我仍在持续更新。\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《语音与语言处理》](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [《神经网络风格迁移》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8493ad10818f.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_968ea215a74a.png)\n\n**300天数据之旅第157天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在此过程中，我学习了计算机视觉、图像分类、CIFAR10 数据集、数据的获取与整理以及数据增强等与之相关的多个主题。除此之外，我还了解了数据抓取与 Scrapy 框架、命名实体识别与 SpaCy 库、使用 SpaCy 训练的 Transformer 模型、地理编码等与之相关的多个主题。目前，我已经完成了神经网络风格迁移笔记本的工作，并开始着手制作关于图像目标识别——CIFAR10 数据集的笔记本。所有相关笔记本如下所示。我在截图中展示了 CIFAR10 数据集的获取与整理过程的实现代码，希望你能从中有所收获，并进一步探索实践。同时，也期待你能够花些时间学习上述及下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《神经网络风格迁移》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)\n  - [《图像目标识别：CIFAR10》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_45df04bc7838.png)\n\n**第158天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了计算机视觉、图像分类、图像增强与过拟合、RGB通道归一化、数据加载器和验证集等主题。此外，我还了解了斯坦福的NER算法、NLTK、命名实体识别等相关内容。我已经完成了使用神经网络进行风格迁移的笔记本项目，并开始着手处理图像中的物体识别：CIFAR10笔记本。所有笔记本都列在下方。我在截图中展示了使用PyTorch实现数据集获取与整理、图像增强及归一化的过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 神经网络风格迁移（[Neural Networks Style Transfer](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNEURAL_STYLE_TRANSFER)）\n  - 图像中的物体识别：CIFAR10（[CIFAR10__Recognition](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dfb2bac7330a.png)\n\n**第159天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书。在这里，我学习了计算机视觉、ResNet模型与残差块、Xavier随机初始化、交叉熵损失函数、训练函数的定义、随机梯度下降、学习率调度器、评估指标等内容。此外，我还花了一些时间阅读《语音与语言处理》一书，在这里我了解了情感分类、逻辑回归中的学习、条件最大似然估计、损失函数等相关内容。目前我正在完成图像中的物体识别：CIFAR10笔记本项目。该笔记本如下所示。我在截图中展示了使用PyTorch定义训练函数的实现过程。希望你能从中获得一些见解，并加以实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 图像中的物体识别：CIFAR10（[CIFAR10__Recognition](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_70b427428b74.png)\n\n**第160天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书。在这里，我学习了ImageNet数据集、数据集的获取与整理、图像增强技术如翻转与缩放、调整亮度与对比度、迁移学习与特征提取、图像归一化等内容。我已经完成了图像中的物体识别：CIFAR10笔记本项目，并开始着手处理犬种识别：ImageNet笔记本。所有笔记本都列在下方。我在截图中展示了使用PyTorch实现图像增强与归一化、定义神经网络模型及损失函数的过程。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 图像中的物体识别：CIFAR10（[CIFAR10__Recognition](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)）\n  - 犬种识别：ImageNet（[DogBreedClassification](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e6aaa60976b9.png)\n\n**第161天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了训练函数的定义、计算机视觉、超参数、随机梯度下降优化函数、学习率调度与优化、训练损失与验证损失等内容。此外，我还花了一些时间阅读《语音与语言处理》一书，在这里我了解了逻辑回归的梯度、SGD算法、小批量训练等相关内容。目前我正在处理犬种识别：ImageNet笔记本项目。该笔记本如下所示。我在截图中展示了使用PyTorch定义训练函数的实现过程。希望你能从中获得一些启发，并继续努力。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[Speech and Language Processing](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n  - 图像中的物体识别：CIFAR10（[CIFAR10__Recognition](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)）\n  - 犬种识别：ImageNet（[DogBreedClassification](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0b985eec3814.png)\n\n**第162天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我继续阅读并实践了《深入浅出深度学习》一书。在这里，我学习了预训练文本表示、词嵌入与Word2Vec、独热编码、Skip-Gram模型及其训练、连续词袋模型及其训练、近似训练、负采样、层次化Softmax、数据集的读取与处理、子采样、词汇表等内容。除此之外，我还阅读了关于如何利用异构编码器改善化学自编码器潜在空间及分子多样性的相关内容。目前我正在处理犬种识别：ImageNet笔记本项目。该笔记本如下所示。我在截图中展示了使用PyTorch实现数据集读取与预处理、子采样以及比较的过程。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 图像中的物体识别：CIFAR10（[CIFAR10__Recognition](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)）\n  - 犬种识别：ImageNet（[DogBreedClassification](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_017633f931fc.png)\n\n**300天数据之旅第163天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了子采样、提取中心目标词与上下文词、最大上下文窗口大小、Penn Tree Bank 数据集以及预训练词嵌入等内容。此外，我还花了一些时间阅读《语音与语言处理》一书，在这里我了解了正则化与过拟合、曼哈顿距离、Lasso 和 Ridge 回归、多项逻辑回归、MLR 中的特征、MLR 的学习过程、模型解释、梯度方程推导等内容。我还完成了“犬种识别：ImageNet”笔记本的工作。在截图中，我展示了使用 PyTorch 提取中心目标词和上下文词的实现。希望你能从中获得一些启发。也希望大家能花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《语音与语言处理》](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n  - [《图像上的物体识别：CIFAR10》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FCIFAR10__Recognition)\n  - [《犬种识别：ImageNet》](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FDogBreedClassification)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8066b1d59975.png)\n\n**300天数据之旅第164天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了子采样与负采样、词嵌入与 Word2Vec、概率、分批读取、拼接与填充、随机小批量等内容。此外，我还花了一些时间阅读《语音与语言处理》一书，在这里我了解了向量语义与嵌入、词汇语义、词元与词义、词义消歧、词语相似度、对比原则、表示学习、同义性等内容。在截图中，我展示了使用 PyTorch 进行负采样的实现。希望你能从中获得一些启发。也希望大家能花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《语音与语言处理》](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ce9f7edf1b0c.png)\n\n**300天数据之旅第165天！**\n- **子采样**：子采样是一种通过选择原始数据的子集来减少数据规模的方法。子集由一个参数决定。子采样旨在尽量减少高频词对词嵌入模型训练的影响。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了词嵌入、批次、损失函数与填充、中心词与上下文词、负采样、数据加载器实例、词汇表、子采样、数据迭代、掩码变量等内容。在截图中，我展示了使用 PyTorch 读取批次以及加载 PTB 数据集的函数的实现。希望你能从中获得一些启发。也希望大家能花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dfb0f379ea0a.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_23204f14d9a8.png)\n\n**300天数据之旅第166天！**\n- **词嵌入**：词嵌入是指用实值向量来表示单词的一种方法，通常用于文本分析。这种向量编码了单词的意义，使得在向量空间中距离较近的单词往往具有相似的含义。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了词嵌入、Word2Vec、Skip Gram 模型、嵌入层、词向量、Skip Gram 模型前向计算、批量矩阵乘法、二元交叉熵损失函数、负采样、掩码变量与填充、模型参数初始化等内容。在截图中，我展示了使用 PyTorch 实现的嵌入层、Skip Gram 模型前向计算以及二元交叉熵损失函数。希望你能从中获得一些启发。也希望大家能花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4f61240b91a6.png)\n\n**300天数据之旅第167天！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了训练 Skip Gram 模型、损失函数、应用词嵌入模型、负采样、使用 Global Vectors 或 Glove 进行词嵌入、条件概率、Glove 模型、交叉熵损失函数等内容。此外，我还花了一些时间阅读《语音与语言处理》一书，在这里我了解了词语相关性、语义场、语义框架与角色、内涵与情感、向量语义、嵌入等内容。在截图中，我展示了使用 PyTorch 训练词嵌入模型的实现。希望你能从中获得一些启发。也希望大家能花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [《语音与语言处理》](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6066b8e18fae.png)\n\n**第168天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了子词嵌入、FastText 和字节对编码、寻找同义词和类比关系、预训练词向量、标记嵌入、中心词与上下文词等主题。此外，我还花了一些时间阅读《语音与语言处理》一书。在这本书中，我学习了词与向量、向量与文档、词项-文档矩阵、信息检索、行向量与上下文矩阵等相关内容。我在截图中展示了使用 PyTorch 实现定义标记嵌入类的代码。希望你能从中获得一些启发。也希望大家能抽出时间学习下面提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[Speech and Language Processing](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_95c9c97c985f.png)\n\n**第169天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了寻找同义词和类比关系、词嵌入模型与 Word2Vec、应用预训练词向量、余弦相似度等主题。同时，我也花了一些时间阅读《语音与语言处理》一书。在这本书中，我了解了用余弦值衡量相似性、点积与内积、向量中词语的权重、词频-逆文档频率（TFIDF）、集合频率、TFIDF 向量模型的应用等内容。我在截图中展示了使用 PyTorch 实现余弦相似度以及寻找同义词和类比关系的代码。希望你能从中获得一些见解。也希望大家能抽出时间学习下面提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[Speech and Language Processing](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a047395bc9f6.png)\n\n**第170天，300天数据之旅！**\n- **来自 Transformer 的双向编码器表示**：ELMO 双向编码上下文，但采用特定任务的架构；GPT 则是任务无关的，仅从左到右编码上下文。而 BERT 不仅双向编码上下文，而且只需对架构进行少量改动即可适用于广泛的自然语言处理任务。在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了 BERT 架构、从上下文无关到上下文敏感、词嵌入模型与 Word2Vec、从任务特定到任务无关、来自语言模型的嵌入或 ELMO 架构、输入表示、标记、片段和位置嵌入以及可学习的位置嵌入等相关内容。我在截图中展示了使用 PyTorch 实现 BERT 输入表示和 BERT 编码器类的代码。希望你能从中获得一些启发。也希望大家能抽出时间学习下面提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dd20a307f3a0.png)\n\n**第171天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了 BERT 编码器类、预训练任务、掩码语言建模、多层感知机、前向推理、BERT 输入序列、双向上下文编码等内容。此外，我还花了一些时间阅读《语音与语言处理》一书。在这本书中，我学习了点互信息（PMI）、拉普拉斯平滑、Word2Vec、带有负采样的跳字模型（SGNS）、分类器、逻辑斯蒂函数与 S 形函数、余弦相似度与点积等相关内容。我在截图中展示了使用 PyTorch 实现掩码语言建模和 BERT 编码器的代码。希望你能从中获得一些启发，并进一步深入研究。也希望大家能抽出时间学习下面提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[Speech and Language Processing](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3725f9012f24.png)\n\n**第172天，300天数据之旅！**\n- **来自 Transformer 的双向编码器表示**：ELMO 双向编码上下文，但采用特定任务的架构；GPT 是任务无关的，仅从左到右编码上下文。而 BERT 则双向编码上下文，且只需对架构进行少量改动即可适应多种 NLP 任务。BERT 的嵌入是标记、片段和位置嵌入三者的总和。在我的机器学习和深度学习旅程中，今天我阅读并实践了《深入浅出深度学习》一书。在这里，我学习了来自 Transformer 的双向编码器表示（即 BERT 架构）、下一句预测模型、交叉熵损失函数、多层感知机、BERT 模型、掩码语言建模、BERT 编码器、BERT 模型的预训练等内容。我在截图中展示了使用 PyTorch 实现下一句预测和 BERT 模型的代码。希望你能从中获得一些启发，并继续深入研究。也希望大家能抽出时间学习下面提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f524168b84ff.png)\n\n**第173天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了BERT模型的预训练及其数据集、定义预训练任务的帮助函数、生成下一句预测任务、生成掩码语言建模任务、序列标记等与之相关的主题。此外，我还花了一些时间阅读《语音与语言处理》一书。书中我了解了跳字嵌入的学习、二分类器、目标与上下文嵌入、嵌入的可视化、嵌入的语义属性等主题。我在截图中展示了使用PyTorch实现的下一句预测任务和掩码语言建模任务的代码。希望你能从中获得一些启发，并加以实践。也希望大家能抽出时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《语音与语言处理》（[Speech and Language Processing](https:\u002F\u002Fweb.stanford.edu\u002F~jurafsky\u002Fslp3\u002F)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e1ea30a7ae19.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_13a5f0c0b928.png)\n\n**第174天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了BERT模型的预训练、下一句预测任务和掩码语言建模任务、将文本转换为预训练数据集等与之相关的主题。此外，我还了解了Scorer以及SpaCy模型的示例实例、长短期记忆神经网络、Smiles向量化器、前馈神经网络等相关内容。我在截图中展示了使用PyTorch将文本转换为预训练数据集的实现过程。希望你能从中获得一些见解，并进一步实践。也希望大家能抽出时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_167c143310f2.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0c30a366455a.png)\n\n**第175天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了BERT模型的预训练、交叉熵损失函数、Adam优化函数、梯度清零、反向传播与优化、掩码语言建模损失和下一句预测损失等与之相关的主题。我在截图中展示了使用PyTorch实现的BERT模型预训练、从BERT模型获取损失以及训练神经网络模型的过程。希望你能从中获得一些启发，并加以实践。也希望大家能抽出时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_257f8f5b09ea.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9217d6be95bf.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_92543bee83dd.png)\n\n**第176天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了自然语言处理的应用、NLP架构与预训练、情感分析及其数据集、文本分类、分词与词汇表、填充至相同长度等与之相关的主题。除此之外，我还了解了命名实体识别、频率分布、NLTK、列表扩展等相关内容。我在截图中展示了使用PyTorch读取数据集、进行分词与构建词汇表、并将文本填充至固定长度的实现过程。希望你能从中获得一些启发，并加以实践。也希望大家能抽出时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《情感分析数据集笔记本》（[Sentiment Analysis Dataset Notebook](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_78434161689b.png)\n\n**第177天，300天数据之旅！**\n- **情感分析**：情感分析是利用自然语言处理、文本分析、计算语言学和生物特征技术，系统地识别、提取、量化和研究情感状态及主观信息的一种方法。它广泛应用于客户声音材料，如评论和调查回复、在线和社交媒体内容，以及医疗保健领域的资料，其应用范围涵盖市场营销、客户服务到临床医学等多个领域。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了创建数据迭代、分词与词汇表、截断与填充、循环神经网络模型与情感分析、预训练词向量与GloVe、双向LSTM与嵌入层、线性层与解码、编码与序列数据、Xavier初始化等与之相关的主题。我在截图中展示了使用PyTorch实现的双向循环神经网络模型。希望你能从中获得一些启发，并加以实践。也希望大家能抽出时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《深入浅出深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《情感分析数据集笔记本》（[Sentiment Analysis Dataset Notebook](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)）\n  - 《基于RNN的情感分析》（[Sentiment Analysis with RNN](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_eff594a46f52.png)\n\n**300天数据之旅第178天！**\n- **情感分析**：情感分析是利用自然语言处理、文本分析、计算语言学和生物特征识别技术，系统地识别、提取、量化并研究情感状态和主观信息。它广泛应用于客户反馈材料（如评论和调查回复）、在线和社交媒体内容以及医疗保健相关资料中，适用于市场营销、客户服务和临床医学等多种场景。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了词向量与词汇表、双向RNN模型的训练与评估、情感分析、一维卷积神经网络、一维互相关运算、时序最大池化层、Text CNN模型、ReLU激活函数和Dropout层等主题。我在截图中展示了使用PyTorch实现文本卷积神经网络的过程。希望你能从中获得一些启发，并进一步实践。同时，也建议你花时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [情感分析数据集笔记本](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n  - [基于RNN的情感分析](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)\n  - [基于CNN的情感分析](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bae991b5be2d.png)\n\n**300天数据之旅第179天！**\n- **自然语言推理**：自然语言推理是指从一个前提推断出假设的过程，其中前提和假设都是文本序列。它用于确定两段文本之间的逻辑关系。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了自然语言推理及其数据集、前提、假设、蕴含、矛盾和中性等概念，还了解了斯坦福自然语言推理数据集、SNLI数据集的读取等内容。我在截图中展示了使用PyTorch读取SNLI数据集的实现过程。希望你能从中获得一些见解，并继续深入探索。同时，也建议你花时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [情感分析数据集笔记本](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n  - [基于RNN的情感分析](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)\n  - [基于CNN的情感分析](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb)\n  - [自然语言推理数据集](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b1e9c72a5c2e.png)\n\n**300天数据之旅第180天！**\n- **自然语言推理**：自然语言推理是指从一个前提推断出假设的过程，其中前提和假设都是文本序列。它用于确定两段文本之间的逻辑关系。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了自然语言推理及SNLI数据集、前提、假设和标签、词汇表、序列的填充与截断、数据集与DataLoader模块等相关知识。此外，我还了解了混淆矩阵与分类报告、文本数据的频率分布和词云等内容。我在截图中展示了使用PyTorch加载SNLI数据集的实现过程。希望你能从中获得一些启发，并继续深入研究。同时，也建议你花时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [情感分析数据集笔记本](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20Dataset.ipynb)\n  - [基于RNN的情感分析](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20RNN.ipynb)\n  - [基于CNN的情感分析](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNeuralNetworks__SentimentAnalysis\u002Fblob\u002Fmaster\u002FPyTorch\u002FSentiment%20Analysis%20CNN.ipynb)\n  - [自然语言推理数据集](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ccdf6f2ee3eb.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c1c01d44ae3e.png)\n\n**300天数据之旅第181天！**\n- **自然语言推理**：自然语言推理是指从一个前提推断出假设的过程，其中前提和假设都是文本序列。它用于确定两段文本之间的逻辑关系。在我的机器学习和深度学习之旅中，今天我阅读并实践了《深入浅出深度学习》一书的内容。在这里，我学习了基于注意力机制的自然语言推理、带有注意力机制的多层感知机（MLP）、前提与假设的对齐、词嵌入与注意力权重等相关知识。我在截图中展示了使用PyTorch实现MLP与注意力机制的过程。希望你能从中获得一些启发，并继续深入研究。同时，也建议你花时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - [《深入浅出深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [自然语言推理数据集](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n  - [自然语言推理](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8aaca1f175f7.png)\n\n**第182天，300天数据之旅！**\n- **比较与聚合类**：比较类将一个序列中的某个词与其软对齐的另一个序列中的对应词进行比较。聚合类则将两组比较向量进行聚合，以推断逻辑关系。它会将两种摘要结果的拼接输入到多层感知机（MLP）中，从而得到逻辑关系的分类结果。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了比较词序列、软对齐、多层感知机（MLP）分类器、聚合比较向量、线性层与拼接、可分解注意力模型、嵌入层等主题。我在截图中展示了使用PyTorch实现的比较类、聚合类以及可分解注意力模型。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对未来充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [自然语言推理数据集](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n  - [自然语言推理：注意力机制](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9bae4b6367af.png)\n\n**第183天，300天数据之旅！**\n- **比较与聚合类**：比较类将一个序列中的某个词与其软对齐的另一个序列中的对应词进行比较。聚合类则将两组比较向量进行聚合，以推断逻辑关系。它会将两种摘要结果的拼接输入到多层感知机（MLP）中，从而得到逻辑关系的分类结果。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了可分解注意力模型、嵌入层和线性层、注意力模型的训练与评估、自然语言推理、蕴含、矛盾与中性、预训练的GloVe词嵌入、SNLI数据集、Adam优化器和交叉熵损失函数、前提与假设等主题。我在截图中展示了使用PyTorch实现的注意力模型训练与评估过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对未来充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [自然语言推理数据集](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNaturalLanguage%20Inference%20Data.ipynb)\n  - [自然语言推理：注意力机制](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3f1029beee25.png)\n\n**第184天，300天数据之旅！**\n- **BERT模型笔记**：对于序列级和标记级的自然语言处理任务，如单文本分类、文本对分类或回归以及文本标注，BERT只需进行极少的架构改动即可适用。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了BERT在序列级和标记级任务上的微调、单文本分类、文本对分类或回归、文本标注、问答、自然语言推理以及预训练的BERT模型、加载预训练的BERT模型及其参数、语义文本相似度、词性标注等主题。我在截图中展示了使用PyTorch加载预训练BERT模型及参数的实现。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对未来充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [自然语言推理：注意力机制](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n  - [自然语言推理：BERT](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8894a89f51d6.png)\n\n**第185天，300天数据之旅！**\n- **BERT模型笔记**：对于序列级和标记级的自然语言处理任务，如单文本分类、文本对分类或回归以及文本标注，BERT只需进行极少的架构改动即可适用。在我的机器学习和深度学习之旅中，今天我阅读并实现了《动手学深度学习》一书中的内容。在这里，我学习了加载预训练的BERT模型及其参数、用于微调BERT模型的数据集、前提、假设和输入序列、分词与词汇表、截断与填充标记、自然语言推理等主题。我在截图中展示了使用PyTorch构建用于微调BERT模型的数据集，并生成训练和测试样本的过程。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对未来充满期待！！\n- 书籍：\n  - [《动手学深度学习》](https:\u002F\u002Fd2l.ai\u002Findex.html)\n  - [自然语言推理：注意力机制](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)\n  - [自然语言推理：BERT](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4e909b016c37.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0a3e266361df.png)\n\n**第186天，300天数据之旅！**\n- **生成对抗网络**：生成对抗网络由两个深度网络组成——生成器和判别器。生成器通过最大化交叉熵损失来生成尽可能接近真实图像的图像，以欺骗判别器；而判别器则通过最小化交叉熵损失来区分生成的图像和真实图像。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了生成对抗网络、生成器和判别器网络、判别器的更新等主题。此外，我还阅读了推荐系统、协同过滤、显式与隐式反馈、推荐任务等相关内容。我在截图中展示了使用PyTorch实现的生成器和判别器网络以及优化过程的简单示例。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《自然语言推理：注意力机制》（[Natural Language Inference: Attention](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)）\n  - 《自然语言推理：BERT模型》（[Natural Language Inference: BERT](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_80a238c82505.png)\n\n**第187天，300天数据之旅！**\n- **生成对抗网络**：生成对抗网络由两个深度网络组成——生成器和判别器。生成器通过最大化交叉熵损失来生成尽可能接近真实图像的图像，以欺骗判别器；而判别器则通过最小化交叉熵损失来区分生成的图像和真实图像。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了生成器和判别器网络、二元交叉熵损失函数、Adam优化器、归一化张量、高斯分布、真实数据与生成数据等相关知识。我在截图中展示了使用PyTorch实现的生成器更新和训练函数的简单示例。希望你能从中获得一些见解，并加以实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《自然语言推理：注意力机制》（[Natural Language Inference: Attention](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20Attention.ipynb)）\n  - 《自然语言推理：BERT模型》（[Natural Language Inference: BERT](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FNatural_Language__Inference\u002Fblob\u002Fmain\u002FNL%20Inference%20BERT.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ddf0c1fa199.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6b11c4728d1e.png)\n\n**第188天，300天数据之旅！**\n- **生成对抗网络**：生成对抗网络由两个深度网络组成——生成器和判别器。生成器通过最大化交叉熵损失来生成尽可能接近真实图像的图像，以欺骗判别器；而判别器则通过最小化交叉熵损失来区分生成的图像和真实图像。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了深度卷积生成对抗网络、宝可梦数据集、图像的缩放与归一化、数据加载器、生成器模块、转置卷积层、批量归一化层、ReLU激活函数等相关知识。此外，我还了解了四分位距、平均绝对偏差、箱线图、密度图、频数表等内容。我在截图中展示了使用PyTorch实现的生成器模块和宝可梦数据集的代码。希望你能从中获得一些启发，并继续深入研究。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《深度卷积GAN》（[Deep Convolutional GAN](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_38abccbc58a3.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_283d63c2aa75.png)\n\n**第189天，300天数据之旅！**\n- **生成对抗网络**：生成对抗网络由两个深度网络组成——生成器和判别器。生成器通过最大化交叉熵损失来生成尽可能接近真实图像的图像，以欺骗判别器；而判别器则通过最小化交叉熵损失来区分生成的图像和真实图像。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了深度卷积生成对抗网络、生成器和判别器网络、Leaky ReLU激活函数及“ReLU死亡”问题、批量归一化、卷积层、步幅与填充等相关知识。我在截图中展示了使用PyTorch实现的判别器模块和生成器模块的代码。希望你能从中获得一些见解，并继续探索。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学深度学习》（[Dive into Deep Learning](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 《深度卷积GAN》（[Deep Convolutional GAN](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7490db015ce5.png)\n\n**第190天，300天数据之旅！**\n- **生成对抗网络**：生成对抗网络由两个深度神经网络组成——生成器和判别器。生成器通过最大化交叉熵损失来生成尽可能接近真实图像的图像，以欺骗判别器；而判别器则通过最小化交叉熵损失来区分生成的图像和真实图像。在我的机器学习和深度学习之旅中，今天我阅读并实践了《动手学深度学习》一书中的内容。在这里，我学习了深度卷积生成对抗网络、生成器和判别器模块、交叉熵损失函数、Adam优化器以及与此相关的其他主题。我在截图中展示了使用PyTorch训练生成器和判别器网络的实现过程。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《动手学深度学习》（[链接](https:\u002F\u002Fd2l.ai\u002Findex.html)）\n  - 深度卷积GAN示例（[链接](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FGAN\u002Fblob\u002Fmain\u002FDeep%20GAN.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ddc226ca43c5.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c04e0d27c9cf.png)\n\n**第191天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，今天我开始阅读并实践《用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了深度学习的实际应用、深度学习的各个领域、神经网络简史、Fastai与Jupyter Notebook、猫狗分类、图像加载器、预训练模型、ResNet和CNN、错误率等主题。我在截图中展示了使用Fastai进行猫狗分类的实现过程。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《用Fastai和PyTorch进行编码的深度学习》\n  - Fastai入门笔记本（[链接](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98c880dcab5b.png)\n\n**第192天，300天数据之旅！**\n- **迁移学习**：迁移学习是指将预训练模型用于与其原始训练任务不同的新任务的过程。微调是迁移学习的一种技术，它通过使用与预训练时不同的任务再训练若干轮，从而更新预训练模型的参数。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了机器学习与权重分配、神经网络与随机梯度下降、机器学习固有的局限性、图像识别、分类与回归、过拟合与验证集、迁移学习、语义分割、情感分类、数据加载器等主题。我在截图中展示了使用Fastai进行语义分割和情感分类的实现过程。希望你能从中获得一些启发，并继续深入研究。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《用Fastai和PyTorch进行编码的深度学习》\n  - Fastai入门笔记本（[链接](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cd1fdeccb560.png)\n\n**第193天，300天数据之旅！**\n- **迁移学习**：迁移学习是指将预训练模型用于与其原始训练任务不同的新任务的过程。微调是迁移学习的一种技术，它通过使用与预训练时不同的任务再训练若干轮，从而更新预训练模型的参数。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了表格数据与分类、表格数据加载器、类别型与连续型数据、推荐系统与协同过滤、模型用的数据集、验证集与测试集、测试集中的评判标准等主题。我在截图中展示了使用Fastai进行表格数据分类和构建推荐系统模型的实现过程。希望你能从中获得一些启发，并继续探索。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《用Fastai和PyTorch进行编码的深度学习》\n  - Fastai入门笔记本（[链接](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F1.%20Introduction.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e20d40852ea5.png)\n\n**第194天，300天数据之旅！**\n- **驱动链方法**：可以概括为：首先明确目标，然后思考为达成该目标可以采取哪些行动，以及现有或可获取的数据如何提供帮助，最后构建一个模型，用以确定最佳行动方案，从而在实现目标方面取得最佳效果。在我的机器学习和深度学习之旅中，今天我阅读并实践了《用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了深度学习的实践、深度学习现状、计算机视觉、文本与自然语言处理、文本与图像结合、表格数据与推荐系统、驱动链方法、数据收集与DuckDuckGo搜索引擎、问卷调查等主题。我在截图中展示了使用DuckDuckGo和Fastai进行目标检测数据收集的实现过程。希望你能从中获得一些启发，并继续深入研究。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《用Fastai和PyTorch进行编码的深度学习》\n  - Fastai图像检测示例（[链接](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2f725981c5c2.png)\n\n**第195天，300天数据之旅！**\n- **传动系统方法**：可以这样概括：首先明确你的目标，然后思考为了达成这个目标你可以采取哪些行动，以及你已经拥有或能够获取哪些有助于实现目标的数据，最后构建一个模型，用以确定最佳行动方案，从而在你的目标范围内取得最佳结果。在我的机器学习和深度学习之旅中，今天我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此过程中，我学习了Fastai的依赖项与函数、有偏数据集、从数据到数据加载器、Data Block API、因变量与自变量、随机划分、图像变换等主题。这里我还展示了如何利用Duck Duck Go和Fastai收集数据并初始化数据加载器的实现。希望你能从中获得一些启发，并进一步深入研究。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：图像检测**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5130330e59fd.png)\n\n**第196天，300天数据之旅！**\n- **数据增强**：数据增强是指通过对输入数据进行随机变换，使其看起来不同，但不改变数据的本质含义。RandomResizedCrop就是数据增强的一个具体例子。在我的机器学习和深度学习之旅中，我继续阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了数据加载器、图像块、图像的缩放、挤压与拉伸、图像填充、数据增强、图像变换、模型训练与误差率、随机缩放与裁剪等相关知识。这里我还展示了使用Fastai实现数据加载器、数据增强以及模型训练的过程。希望你能从中获得一些见解，并加以实践。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：图像检测**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7fa2566ab205.png)\n\n**第197天，300天数据之旅！**\n- **数据增强**：数据增强是指通过对输入数据进行随机变换，使其看起来不同，但不改变数据的本质含义。RandomResizedCrop就是数据增强的一个具体例子。在我的机器学习和深度学习之旅中，我继续阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了预训练模型的微调、数据增强与变换、分类结果解释与混淆矩阵、数据集清洗、推理模型与参数、Notebook与Widgets等相关知识。这里我还展示了使用Fastai实现分类结果解释、数据集清洗、推理模型与参数的简单应用。希望你能从中获得一些启发，并进一步探索。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：图像检测**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F2.%20Model%20Production\u002FBearDetector.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5be9a2457236.png)\n\n**第198天，300天数据之旅！**\n- **数据伦理**：伦理是指关于善恶的合理标准，它规定了人类应当如何行事。它是对个人伦理标准的研究与发展。申诉机制、反馈循环、偏见等都是数据伦理的重要体现。在我的机器学习和深度学习之旅中，我继续阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了数据伦理、错误与申诉、反馈循环、偏见、将机器学习融入产品设计、数字分类器的训练、像素与计算机视觉、坚韧精神与深度学习、像素相似性、列表推导式等相关知识。这里我还展示了使用Fastai实现像素与计算机视觉的简单应用。希望你能从中获得一些启发，并加以实践。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c3f25089d26a.png)\n\n**第199天，300天数据之旅！**\n- **L1范数与L2范数**：取差值绝对值的平均值称为平均绝对差，即L1范数；而取差值平方的平均值后再开方，则称为均方根误差，即L2范数。在我的机器学习和深度学习之旅中，我继续阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了张量的秩、平均绝对差（L1范数）与均方根误差（L2范数）、Numpy数组与PyTorch张量、利用广播机制计算指标等相关知识。这里我还展示了使用Fastai实现数组与张量、L1与L2范数的简单应用。希望你能从中获得一些启发，并进一步探索。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6262ed4e270e.png)\n\n**300天数据之旅第200天！**\n- **L1范数和L2范数**：将差值的绝对值取平均，称为平均绝对误差或L1范数。将差值的平方取平均后再开方，称为均方根误差或L2范数。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了如何利用广播机制计算指标、平均绝对误差、随机梯度下降、参数初始化、损失函数、梯度计算、反向传播与导数、学习率优化等主题。我在截图中展示了使用Fastai实现的简单随机梯度下降算法。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fe06b9bfab42.png)\n\n**300天数据之旅第201天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了梯度下降过程、参数初始化、预测计算与检查、损失及均方误差的计算、梯度计算与反向传播、权重更新与参数调整、重复迭代与停止条件等相关内容。我在截图中展示了使用Fastai和PyTorch实现的梯度下降过程。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6f1d7e5c54f1.png)\n\n**300天数据之旅第202天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了MNIST损失函数、矩阵与向量、自变量、权重与偏置、参数、矩阵乘法与数据集类、梯度下降过程与学习率、激活函数等相关内容。我在截图中展示了使用Fastai和PyTorch实现的数据集类和矩阵乘法。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_10b2699df2b0.png)\n\n**300天数据之旅第203天！**\n- **准确率与损失函数**：准确率等指标与损失函数的关键区别在于，损失函数用于驱动自动化学习，而指标则用于帮助人类理解模型的表现。损失函数必须是一个具有有意义导数的函数，而指标则侧重于模型的性能评估。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了矩阵乘法、激活函数、损失函数、梯度与斜率、Sigmoid函数、准确率指标及其理解等相关内容。我在截图中展示了使用Fastai和PyTorch实现的损失函数与Sigmoid函数。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ebc7f3651236.png)\n\n**300天数据之旅第204天！**\n- **SGD与小批量**：根据梯度来调整或更新权重，以考虑下一轮学习过程中涉及的一些细节的过程，称为优化步骤。每次对少量数据计算平均损失的操作称为一个小批量。小批量中包含的数据数量称为批次大小。较大的批次大小能够提供更准确、更稳定的基于损失函数的梯度估计，而单个样本的批次则会导致不精确且不稳定的梯度。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了随机梯度下降与小批量、优化步骤、批次大小、数据加载器与数据集、参数初始化、权重与偏置、反向传播与梯度、损失函数等相关内容。我在截图中展示了使用Fastai和PyTorch实现的数据加载器与梯度计算。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1254ac6d5eae.png)\n\n**第205天，300天数据之旅！**\n- **SGD与小批量**：根据梯度来调整或更新权重的过程，以便在学习过程的下一阶段考虑一些细节，这一过程被称为优化步骤。每次对少量数据计算平均损失称为一个小批量。小批量中包含的数据项数量称为批次大小。较大的批次大小意味着可以从损失函数更准确、更稳定地估计数据集的梯度；而单个样本的批次则会导致梯度不精确且不稳定。在我的机器学习和深度学习旅程中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了梯度计算与反向传播、权重、偏置与参数、梯度清零、训练循环与学习率、准确率与评估、创建优化器等主题。我在截图中展示了使用Fastai和PyTorch进行梯度计算、准确率评估和训练的实现。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5b8a90871b50.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2dd9b98b8b35.png)\n\n**第206天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了创建优化器、线性模块、权重与偏置、模型参数、优化与梯度清零、SGD类、数据加载器以及Fastai的学习者类等相关内容。我在截图中展示了使用Fastai和PyTorch创建优化器和学习者类的实现。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3204337a034f.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4122733442ae.png)\n\n**第207天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了添加非线性、简单线性分类器、基础神经网络、权重与偏置张量、修正线性单元（ReLU）激活函数、通用逼近定理、序列模块等相关内容。我在截图中展示了使用Fastai和PyTorch创建简单神经网络的实现。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：训练分类器**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F3.%20Training%20a%20Classifier\u002FDigitClassifier.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_509a0894cc69.png)\n\n**第208天，300天数据之旅！**\n- 在我的机器学习和深度学习旅程中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了图像分类、定位、正则表达式、数据块与数据加载器、正则表达式标签器、数据增强、预缩放、检查与调试数据块、项目与批次变换等相关内容。我在截图中展示了使用Fastai和PyTorch创建并调试数据块及数据加载器的实现。我将Resize用作大尺寸的项目变换，而将RandomResizedCrop用作小尺寸的批次变换。如果aug transforms函数中传入了min scale参数，如下面的DataBlock调用所示，则会添加RandomResizedCrop。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：图像分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_acace20ad7b8.png)\n\n**第209天，300天数据之旅！**\n- **指数函数**：指数函数被定义为e\\*\\*x，其中e是一个特殊的数字，约等于2.718。它是自然对数函数的逆函数。指数函数始终为正，并且增长非常迅速。在我的机器学习和深度学习旅程中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了交叉熵损失函数、查看激活值与标签、Softmax激活函数、Sigmoid函数、指数函数、负对数似然、二分类等相关内容。我在截图中展示了使用Fastai和PyTorch实现Softmax函数和负对数似然的代码。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：图像分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_42822e5dc8a6.png)\n\n**300天数据之旅第210天！**\n- **指数函数**：指数函数定义为 e\\*\\*x，其中 e 是一个特殊的数，约等于 2.718。它是自然对数函数的反函数。指数函数始终为正，并且增长非常迅速。在我的机器学习和深度学习之旅中，我阅读并实践了《使用 Fastai 和 PyTorch 的编码者深度学习》一书的内容。在这里，我学习了对数函数、负对数似然、交叉熵损失函数、Softmax 函数、模型解释、混淆矩阵、模型改进、学习率查找器、对数尺度等主题。我在截图中展示了使用 Fastai 和 PyTorch 实现的交叉熵损失、混淆矩阵和学习率查找器。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用 Fastai 和 PyTorch 的编码者深度学习》\n  - [**Fastai：图像分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e18f4b7c5a0e.png)\n\n**300天数据之旅第211天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用 Fastai 和 PyTorch 的编码者深度学习》一书的内容。在这里，我学习了解冻与迁移学习、冻结预训练层、区分性学习率、选择 epoch 数量、更深的网络架构等相关内容。我在截图中展示了使用 Fastai 和 PyTorch 实现的解冻与迁移学习以及区分性学习率。希望你能从中获得一些见解，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用 Fastai 和 PyTorch 的编码者深度学习》\n  - [**Fastai：图像分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F4.%20Image%20Classification\u002FImageClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_40e2cb2cc6ea.png)\n\n**300天数据之旅第212天！**\n- **多标签分类**：多标签分类是指识别图像中可能包含多种类别对象的问题。在我的机器学习和深度学习之旅中，我阅读并实践了《使用 Fastai 和 PyTorch 的编码者深度学习》一书的内容。在这里，我学习了图像分类问卷、多标签分类与回归、Pascal 数据集、Pandas 和 DataFrame、构建 DataBlock、数据集和数据加载器、Lambda 函数等相关内容。我在截图中展示了使用 Fastai 和 PyTorch 实现的创建 DataBlock 和数据加载器的过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用 Fastai 和 PyTorch 的编码者深度学习》\n  - [**Fastai：多标签分类与回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2aaecf1df3d4.png)\n\n**300天数据之旅第213天！**\n- **多标签分类**：多标签分类是指识别图像中可能包含多种类别对象的问题。在我的机器学习和深度学习之旅中，我阅读并实践了《使用 Fastai 和 PyTorch 的编码者深度学习》一书的内容。在这里，我学习了 Lambda 函数、图像块和多类别块等转换块、独热编码、数据划分、数据加载器、数据集和 DataBlock、调整大小和裁剪等相关内容。我在截图中展示了使用 Fastai 和 PyTorch 实现的创建 DataBlock 和数据加载器的过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用 Fastai 和 PyTorch 的编码者深度学习》\n  - [**Fastai：多标签分类与回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bebab06b9d66.png)\n\n**300天数据之旅第214天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用 Fastai 和 PyTorch 的编码者深度学习》一书的内容。在这里，我学习了二元交叉熵损失函数、数据加载器和学习器、获取模型激活值、Sigmoid 和 Softmax 函数、独热编码、计算准确率、部分函数等相关内容。**F.binary_cross_entropy** 及其模块等效形式 **nn.BCELoss** 会在独热编码的目标上计算交叉熵，但不包含初始的 Sigmoid 操作。通常，**F.binary_cross_entropy_with_logits** 或 **nn.BCEWithLogitsLoss** 会将 Sigmoid 和二元交叉熵合并到一个函数中进行计算。类似地，对于单标签数据集，**F.nll_loss** 或 **nn.NLLoss** 用于不包含初始 Softmax 的版本，而 **F.cross_entropy** 或 **nn.CrossEntropyLoss** 则用于包含初始 Softmax 的版本。我在截图中展示了使用 Fastai 和 PyTorch 实现的交叉熵损失函数和准确率计算。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用 Fastai 和 PyTorch 的编码者深度学习》\n  - [**Fastai：多标签分类与回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_84076ba68fe8.png)\n\n**300天数据之旅第215天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了多标签分类与阈值、Sigmoid激活函数、过拟合、图像回归、验证损失与指标、部分函数等主题。其中，**F.binary_cross_entropy**及其模块等价形式**nn.BCELoss**会在one-hot编码的目标上计算交叉熵，但不包含初始的Sigmoid操作。通常，**F.binary_cross_entropy_with_logits**或**nn.BCEWithLogitsLoss**则在一个函数中同时完成Sigmoid和二元交叉熵的计算。类似地，对于单标签数据集，可以使用**F.nll_loss**或**nn.NLLoss**（不含初始Softmax版本）以及**F.cross_entropy**或**nn.CrossEntropyLoss**（含初始Softmax版本）。我在截图中展示了使用Fastai和PyTorch训练卷积网络，并结合准确率与阈值的实现。希望你能从中获得一些启发，并进一步实践。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：多标签分类与回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n  - [**Fastai：图像回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FRegression.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_011d851b4a1a.png)\n\n**300天数据之旅第216天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了图像回归与定位、数据集的组装、DataBlock和DataLoaders的初始化、点标注与数据增强、模型训练、Sigmoid范围、MSE损失函数、迁移学习等主题。我在截图中展示了使用Fastai和PyTorch进行DataBlock和DataLoaders初始化，以及图像回归训练的实现。希望你能从中获得一些见解，并加以实践。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**Fastai：多标签分类与回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FMultilabelClassification.ipynb)\n  - [**Fastai：图像回归**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F5.%20MultilabelClassification%20Regression\u002FRegression.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9f337e9727a4.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b0b3e7558a72.png)\n\n**300天数据之旅第217天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了Imagenette分类、DataBlock和DataLoaders、数据归一化与归一化函数、渐进式调整大小与数据增强、迁移学习、均值与标准差等主题。**渐进式调整大小**是指随着训练的进行，逐步使用更大尺寸的图像。我在截图中展示了使用Fastai和PyTorch进行DataBlock和DataLoaders初始化、数据归一化以及渐进式调整大小的实现。希望你能从中获得一些启发，并加以实践。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**高级分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F6.%20Advanced%20Classification\u002FImagenetteClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9152fe19416b.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3fc18a6274f3.png)\n\n**300天数据之旅第218天！**\n- **标签平滑**：标签平滑是一种在训练过程中将所有的标签1替换为略小于1的数值，将0替换为略大于0的数值的方法。这种方法可以使训练更加稳健，即使存在标签错误的数据，也能得到一个在推理时泛化能力更强的模型。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了渐进式调整大小、测试时数据增强、Mixup数据增强、线性组合、回调函数、标签平滑与交叉熵损失函数等主题。在推理或验证阶段，通过数据增强为每张图像生成多个版本，然后对每个增强版本的预测结果取平均值或最大值，这一过程被称为**测试时数据增强**。我在截图中展示了使用Fastai和PyTorch进行渐进式调整大小、测试时数据增强、Mixup数据增强以及标签平滑的实现。希望你能从中获得一些启发，并加以实践。也建议花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**高级分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F6.%20Advanced%20Classification\u002FImagenetteClassification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2cd593b58433.png)\n\n**300天数据之旅第219天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了协同过滤、隐因子的学习、损失函数与随机梯度下降、创建DataLoader、批次处理、点积与矩阵乘法等主题。将两个向量的对应元素相乘后再求和的数学运算称为**点积**。我在截图中展示了如何使用Fastai和PyTorch初始化数据集并创建DataLoader的实现。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**协同过滤**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9bbcb0faf630.png)\n\n**300天数据之旅第220天！**\n- **嵌入层**：这种特殊的层通过整数索引到一个向量中，但其导数的计算方式却与用独热编码向量进行矩阵乘法时完全相同，这样的层就被称为**嵌入层**。利用一种计算捷径，可以直接通过索引来完成与独热编码矩阵的乘法运算。而负责执行这种乘法运算的矩阵则被称为**嵌入矩阵**。在我的机器学习和深度学习之旅中，我继续阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了创建DataLoader、嵌入矩阵、协同过滤、Python面向对象编程、继承、模块与前向传播函数、批次与Learner、Sigmoid范围等相关内容。我在截图中展示了使用Fastai和PyTorch实现的嵌入层、点积类以及Sigmoid范围的代码。希望你能从中获得一些见解，并进一步实践。也希望大家能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**协同过滤**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_94eec8b48259.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_686065918219.png)\n\n**300天数据之旅第221天！**\n- **嵌入层**：这种特殊的层通过整数索引到一个向量中，但其导数的计算方式却与用独热编码向量进行矩阵乘法时完全相同，这样的层就被称为**嵌入层**。利用一种计算捷径，可以直接通过索引来完成与独热编码矩阵的乘法运算。而负责执行这种乘法运算的矩阵则被称为**嵌入矩阵**。在我的机器学习和深度学习之旅中，我继续阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了协同过滤、权重衰减或L2正则化、过拟合、创建嵌入层与权重矩阵、参数模块等相关内容。**权重衰减**是指在损失函数中加入权重平方和的项。其原理是：系数越大，损失函数中的“峡谷”就越陡峭。我在截图中展示了使用Fastai和PyTorch实现的偏置、权重衰减及权重矩阵的代码。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**协同过滤**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2373a9587857.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_19a1e3dbc4dc.png)\n\n**300天数据之旅第222天！**\n- **嵌入层**：这种特殊的层通过整数索引到一个向量中，但其导数的计算方式却与用独热编码向量进行矩阵乘法时完全相同，这样的层就被称为**嵌入层**。利用一种计算捷径，可以直接通过索引来完成与独热编码矩阵的乘法运算。而负责执行这种乘法运算的矩阵则被称为**嵌入矩阵**。在我的机器学习和深度学习之旅中，我继续阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了嵌入层与偏置的解读、主成分分析（PCA）、协作学习模型、嵌入距离与余弦相似度、协同过滤模型的自举法、概率矩阵分解或点积模型等相关内容。我在截图中展示了使用Fastai和PyTorch实现的偏置解读、协作学习模型以及嵌入距离的代码。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**协同过滤**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7f6a6d44c7ca.png)\n\n**300天数据之旅第223天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了深度学习与协同过滤、嵌入矩阵、线性函数、ReLU及非线性函数、Sigmoid范围、前向传播函数、表格模型和嵌入神经网络等相关主题。在Python中，参数列表中的`kwargs`表示“将任何额外的关键字参数放入名为`kwargs`的字典中”。而调用时的`kwargs`则表示“将`kwargs`字典中的所有键值对作为命名参数插入此处”。我在截图中展示了使用Fastai和PyTorch实现的协同过滤和神经网络的深度学习应用。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**协同过滤**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F7.%20Collaborative%20Filtering\u002FCollaborativeFiltering.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3793de974678.png)\n\n**300天数据之旅第224天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了表格建模、分类嵌入、连续与分类变量、推荐系统、表格数据集、序数列、决策树、日期处理、表格Pandas以及表格Proc对象等相关主题。我在截图中展示了使用Fastai和PyTorch实现的日期处理、表格Pandas和表格Proc的应用。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**表格建模**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6fc0fc03c2f0.png)\n\n**300天数据之旅第225天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了表格建模、创建决策树、叶节点、均方根误差、DTreeviz库、停止准则、过拟合等相关主题。我在截图中展示了使用Fastai和PyTorch实现的创建决策树和叶节点的过程。希望你能从中获得一些 insights，并继续深入实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**表格建模**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d8d7d6f6c462.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c893c73b5ef4.png)\n\n**300天数据之旅第226天！**\n- **随机森林**：随机森林是一种通过平均大量决策树预测结果的模型，这些决策树是通过随机调整各种参数生成的，这些参数决定了用于训练树的数据以及其他树的配置。Bagging是一种特殊的集成方法，用于将多个模型的结果结合起来。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了分类变量、随机森林与Bagging预测器、集成、最优参数、袋外误差、用于预测置信度的树方差与标准差、模型解释等相关主题。“袋外误差”（OOB误差）是一种在训练数据集中衡量预测误差的方法，它只将那些未被包含在训练中的样本对应的树的误差计入计算。我在截图中展示了使用Fastai和PyTorch实现的随机森林构建及模型解释过程。希望你能从中获得一些启示，并继续实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**表格建模**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_df1968d9d949.png)\n\n**300天数据之旅第227天！**\n- **随机森林**：随机森林是一种通过平均大量决策树预测结果的模型，这些决策树是通过随机调整各种参数生成的，这些参数决定了用于训练树的数据以及其他树的配置。Bagging是一种特殊的集成方法，用于将多个模型的结果结合起来。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了随机森林、特征重要性、移除低重要性变量、移除冗余特征、确定特征相似性、秩相关性、OOB评分等相关主题。我在截图中展示了使用Fastai和PyTorch实现的随机森林及特征重要性的应用。希望你能从中获得一些 insights，并继续深入实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**表格建模**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d23f210cbb67.png)\n\n**300天数据之旅第228天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书。在这里，我学习了去除冗余特征、确定相似性、OOB得分、部分依赖图、数据泄露、均方根误差等内容。通过计算各棵树预测结果的标准差，可以衡量预测的相对置信度；标准差越小，模型的一致性越高。我在截图中展示了使用Fastai和PyTorch实现去除冗余特征及部分依赖图的过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [表格建模](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5d36298e0586.png)\n\n**300天数据之旅第229天！**\n- **随机森林模型**只是简单地对多棵树的预测结果取平均，因此它永远无法预测训练数据范围之外的值。**随机森林**无法对外部数据类型进行外推，即无法处理域外数据。这里的“预测”就是随机森林直接做出的预测结果；而“偏差”则是基于因变量均值得出的预测。同样，“贡献”则表示每个自变量对最终预测结果的总影响。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书。在此，我学习了树解释器、冗余特征、瀑布图、随机森林、预测、偏差与贡献、外推问题、unsqueeze方法、域外数据等内容。我在截图中展示了使用Fastai和PyTorch实现树解释器、瀑布图以及外推问题的过程。希望你能从中获得一些洞见，并进一步探索。也建议你花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [表格建模](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_64724dbae904.png)\n\n**300天数据之旅第230天！**\n- **随机森林模型**只是简单地对多棵树的预测结果取平均，因此它永远无法预测训练数据范围之外的值。**随机森林**无法对外部数据类型进行外推，即无法处理域外数据。这里的“预测”就是随机森林直接做出的预测结果；而“偏差”则是基于因变量均值得出的预测。同样，“贡献”则表示每个自变量对最终预测结果的总影响。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书。在此，我学习了外推问题与随机森林、识别域外数据、均方根误差与特征重要性、直方图等内容。我在截图中展示了使用Fastai和PyTorch实现识别域外数据及RMSE的过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [表格建模](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ed3c266f49d2.png)\n\n**300天数据之旅第231天！**\n- **随机森林**：随机森林模型只是简单地对多棵树的预测结果取平均，因此它永远无法预测训练数据范围之外的值。随机森林无法对外部数据类型进行外推，即无法处理域外数据。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书。在此，我学习了表格建模与神经网络、连续型与类别型特征、嵌入矩阵、均方误差与回归、表格学习器与学习率、集成学习、Bagging与Boosting、嵌入组合等内容。集成学习是一种泛化技术，通过结合多个模型的预测结果来提高整体性能。我在截图中展示了使用Fastai和PyTorch实现表格建模与神经网络以及集成学习的过程。希望你能从中获得一些启发，并继续深入研究。也建议你花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [表格建模](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F8.%20Tabular%20Modeling\u002FTabularModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9fa9bc330bba.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_cbcfd3c5cc85.png)\n\n**300天数据之旅第232天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书。在此，我学习了自然语言处理与语言模型、自监督学习、文本预处理、分词、数值化与嵌入矩阵、子词与字符、标记等内容。其中，“标记”是分词过程生成的列表中的一个元素，它可以是一个完整的词、词的一部分、子词或单个字符。我在截图中展示了使用Fastai和PyTorch加载数据及进行单词分词的实现过程。希望你能从中获得一些启发，并继续深入学习。也建议你花些时间学习下方提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [自然语言处理](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_831ca4b3e4b1.png)\n\n**300天数据之旅第233天！**\n- **分词**：**子词分词**根据最常出现的子字符串将单词拆分成更小的部分。**单词分词**则不仅按空格分割句子，还会应用特定语言的规则，在没有空格的情况下尝试分离语义单元。**子词分词**提供了一种在字符级分词（使用较小的子词汇表）和单词级分词（使用较大的子词汇表）之间灵活切换的方式，并且无需为每种语言单独开发算法即可处理所有人类语言。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此书中，我学习了关于单词分词、子词分词、设置方法、词汇表、使用Fastai进行数值化、嵌入矩阵等内容。我在截图中展示了使用Fastai和PyTorch实现子词分词与数值化的过程。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [自然语言处理](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_33e73d46cbd5.png)\n\n**300天数据之旅第234天！**\n- **分词**：**子词分词**根据最常出现的子字符串将单词拆分成更小的部分。**单词分词**则不仅按空格分割句子，还会应用特定语言的规则，在没有空格的情况下尝试分离语义单元。**子词分词**提供了一种在字符级分词（使用较小的子词汇表）和单词级分词（使用较大的子词汇表）之间灵活切换的方式，并且无需为每种语言单独开发算法即可处理所有人类语言。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此书中，我学习了关于使用Fastai进行数值化、嵌入矩阵、为语言模型创建批次、分词、训练文本分类器、利用DataBlock构建语言模型、数据加载器、微调语言模型及迁移学习等内容。我在截图中展示了使用Fastai和PyTorch为语言模型创建数据加载器和Data Block的实现过程。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [自然语言处理](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e13597531fcb.png)\n\n**300天数据之旅第235天！**\n- **编码器**：编码器是指不包含任务特定输出层的模型。在视觉CNN中，“编码器”一词与“主体”含义相近，但在NLP和生成模型中，“编码器”这一术语更为常用。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此书中，我学习了关于编码器模型、文本生成与分类、创建分类器数据加载器、嵌入、数据增强、分类器的微调、区分性学习率与逐步解冻、虚假信息与语言模型等内容。我在截图中展示了使用Fastai和PyTorch，通过区分性学习率与逐步解冻来训练文本分类器模型的过程。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [自然语言处理](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F9.%20Natural%20Language%20Processing\u002FNLP.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f290d62dccff.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5fea1444b137.png)\n\n**300天数据之旅第236天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此书中，我学习了关于使用Fastai进行数据清洗、分词与数值化、创建数据加载器和Data Block、中级API、变换、解码方法、数据增强、裁剪与填充等内容。我在截图中展示了使用Fastai和PyTorch进行数据加载器创建、分词与数值化的实现过程。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [数据清洗](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dde5bdbf7153.png)\n\n**300天数据之旅第237天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此书中，我学习了关于数据清洗、装饰器、流水线方法、转换后的集合、训练集与验证集、数据加载器对象、分类方法、变换等内容。我在截图中展示了使用Fastai和PyTorch实现流水线类和转换后的集合的过程。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [数据清洗](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fc28d9c310a8.png)\n\n**300天数据之旅第238天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了数据集类、转换集合、管道、分类方法、数据加载器和数据块、文本块、部分函数、类别块等主题。这里我展示了使用Fastai和PyTorch实现的数据集类、转换集合和数据加载器的代码示例。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**数据清洗**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_454a705b7cc8.png)\n\n**300天数据之旅第239天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了如何将中级数据API应用于暹罗对和计算机视觉、数据加载器、变换与图像调整大小、数据增强、子类、转换集合等主题。数据集类可以同时对同一原始对象应用两个或多个管道，并将结果组合成一个元组。它会自动完成设置并将数据索引到数据集中。这里我展示了使用Fastai和PyTorch实现的暹罗图像对象及数据增强的代码示例。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**数据清洗**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_5e5c4b28722f.png)\n\n**300天数据之旅第240天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了暹罗变换对象、随机拆分、转换集合与数据集类、数据加载器、ToTensor方法和IntToFloatTensor方法、数据归一化与批量归一化等主题。ToTensor方法用于将图像转换为张量。IntToFloatTensor方法则将包含0至255整数的图像张量转换为浮点张量，并除以255使值范围在0到1之间。这里我展示了使用Fastai和PyTorch实现的暹罗变换对象及数据增强的代码示例。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**数据清洗**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F10.%20Data%20Munging\u002FDataMunging.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_6d6361260f51.png)\n\n**300天数据之旅第241天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了从零开始构建语言模型、数据拼接与分词、词汇表与数值化、神经网络、自变量与因变量、张量序列等主题。这里我展示了使用Fastai和PyTorch准备语言模型张量序列的代码示例。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始构建语言模型**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8ad202ee6a65.png)\n\n**300天数据之旅第242天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了使用PyTorch从零开始构建语言模型、张量序列、创建数据加载器与设置批次大小、神经网络架构与线性层、词嵌入与激活函数、权重矩阵、创建学习者与训练等主题。我将构建一个神经网络架构，该架构以三个词作为输入，输出每个可能的下一个词的概率预测。我将使用三个标准的线性层。第一层仅使用第一个词的词嵌入作为激活；第二层使用第二个词的词嵌入加上第一层的输出激活；第三层则使用第三个词的词嵌入加上第二层的输出激活。其关键在于，每个词都会根据其前面的词所处的信息上下文来被解释。这三个层将共享同一权重矩阵。这里我展示了使用Fastai和PyTorch实现的数据加载器创建、从零开始构建语言模型以及训练的代码示例。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始构建语言模型**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_2eb457924c4c.png)\n\n**第243天，300天数据之旅！**\n- **时间反向传播**：时间反向传播是一种将每个时间步都视为一层的神经网络当作一个大型模型来处理，并以常规方式计算其梯度的方法。为了避免内存和时间耗尽，BPTT技术会每隔几个时间步断开隐藏状态中计算步骤的历史记录。隐藏状态被定义为循环神经网络每一步更新的激活值。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了循环神经网络、NN的隐藏状态、RNN的改进、RNN状态的维护、展开表示、反向传播与导数、detach方法、有状态RNN、时间反向传播等主题。我在截图中展示了使用Fastai和PyTorch实现的循环神经网络和语言模型。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [从零开始构建语言模型](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7020d0060ef7.png)\n\n**第244天，300天数据之旅！**\n- **时间反向传播**：时间反向传播是一种将每个时间步都视为一层的神经网络当作一个大型模型来处理，并以常规方式计算其梯度的方法。为了避免内存和时间耗尽，BPTT技术会每隔几个时间步断开隐藏状态中计算步骤的历史记录。隐藏状态被定义为循环神经网络每一步更新的激活值。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了时间反向传播、LMDataLoader对象及数据集整理、数据加载器的创建、回调函数与重置方法、增加信号等内容。我在截图中展示了使用Fastai和PyTorch实现的数据集整理、数据加载器创建、回调函数与重置方法等内容。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [从零开始构建语言模型](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4bc80f743784.png)\n\n**第245天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了创建更多信号或序列、交叉熵损失函数与flatten方法、多层循环神经网络与激活函数、展开表示、Stack等内容。单层循环神经网络的表现优于多层循环神经网络，因为更深的模型容易导致激活值爆炸或消失。我在截图中展示了使用Fastai和PyTorch实现的创建更多信号和多层循环神经网络的内容。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [从零开始构建语言模型](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7c5c8a29038e.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b72e6a4f923c.png)\n\n**第246天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了激活值爆炸与消失、矩阵乘法、长短期记忆网络与RNN的架构、Sigmoid和Tanh函数、隐藏状态与细胞状态、遗忘门、输入门、细胞门和输出门、Chunk方法等内容。我在截图中展示了使用Fastai和PyTorch实现的长短期记忆网络。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [从零开始构建语言模型](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_354e95bca695.png)\n\n**第247天，300天数据之旅！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了使用LSTM训练语言模型、嵌入层、线性层、LSTM的过拟合与正则化、Dropout正则化、训练与推理、伯努利方法等内容。**Dropout**是一种正则化技术，在训练时会随机将部分激活值设为零。我在截图中展示了使用Fastai和PyTorch实现的基于长短期记忆网络和Dropout的语言模型。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [从零开始构建语言模型](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e3902c717bbd.png)\n\n**300天数据之旅第248天！**\n- **激活正则化**：激活正则化是在LSTM产生的最终激活值上添加一个小的惩罚项，以使其尽可能小。这是一种与权重衰减非常相似的正则化方法。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书中的内容。在这里，我了解了激活正则化和时间激活正则化、基于长短期记忆网络的语言模型、权重衰减、训练权重共享的正则化LSTM、权重共享与输入嵌入、文本学习器、交叉熵损失函数以及与此相关的其他主题。我在截图中展示了使用Fastai和PyTorch实现的正则化长短期记忆语言模型，以及正则化的Dropout和激活正则化。希望你能从中获得一些启发，并进一步实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [从零开始构建语言模型](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F11.%20Language%20Model\u002FLanguageModel.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_dcefe63cd11e.png)\n\n**300天数据之旅第249天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书中的内容。在这里，我学习了卷积神经网络、卷积的魔力、特征工程、卷积核与矩阵、卷积核映射、嵌套列表推导式、矩阵乘法等主题。特征工程是指通过对输入数据进行新的变换，使其更易于建模的过程。我在截图中展示了使用Fastai和PyTorch实现的特征工程和卷积核映射。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [卷积神经网络](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_31ad8c0d0683.png)\n\n**300天数据之旅第250天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书中的内容。在这里，我学习了使用PyTorch进行卷积、张量的秩、创建数据块和数据加载器、图像的通道、unsqueeze方法与单位轴、步幅与填充、理解卷积方程、矩阵乘法、权重共享等主题。通道是图像中的单一基本颜色。对于普通的全彩色图像，有三个通道：红色、绿色和蓝色。传递给卷积的卷积核必须是4阶张量。我在截图中展示了使用Fastai和PyTorch实现的卷积和数据加载器。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [卷积神经网络](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_104c545c77e5.png)\n\n**300天数据之旅第251天！**\n- **通道与特征**：通道与特征这两个术语经常可以互换使用，它们指的是权重矩阵第二轴的大小，也就是卷积后每个网格单元的激活数量。通道通常指输入数据，即网络内部的颜色或激活值。使用步幅为2的卷积往往会增加特征的数量，因为激活图中的激活数量会减少到原来的四分之一。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书中的内容。在这里，我学习了卷积神经网络、重构、通道与特征、理解卷积运算、偏置、感受野、对RGB图像的卷积、随机梯度下降等主题。我在截图中展示了使用Fastai和PyTorch实现的卷积神经网络及学员训练过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [卷积神经网络](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1b28badf73b8.png)\n\n**300天数据之旅第252天！**\n- **通道与特征**：通道与特征这两个术语经常可以互换使用，它们指的是权重矩阵第二轴的大小，也就是卷积后每个网格单元的激活数量。通道通常指输入数据，即网络内部的颜色或激活值。使用步幅为2的卷积往往会增加特征的数量，因为激活图中的激活数量会减少到原来的四分之一。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书中的内容。在这里，我学习了提高卷积神经网络训练稳定性、批量大小与数据集划分、简单基准网络、激活与卷积核尺寸、激活统计回调、学习率、创建学员及训练等主题。我在截图中展示了使用Fastai和PyTorch实现的卷积神经网络及学员训练过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [卷积神经网络](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_37b0ce4a19ee.png)\n\n**300天数据之旅第253天！**\n- **单周期训练**：单周期训练是预热和退火的结合。预热阶段，学习率从最小值逐渐增加到最大值；退火阶段，则从最大值逐步降低回最小值。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书中的内容。在此过程中，我学习了激活统计回调、增大批次大小、激活函数、单周期训练、预热与退火、超级收敛、学习率与动量、彩色维度及直方图等主题。这里我通过截图展示了利用Fastai和PyTorch实现的增大批次大小、单周期训练以及检查动量和激活的过程。希望你能从中获得一些启发，并进一步实践。也建议你花些时间深入学习书中提到的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - 【卷积神经网络】(https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F12.%20Convolutional%20Neural%20Networks\u002FCNN.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_09c565db633d.png)\n\n**300天数据之旅第254天！**\n- **全卷积网络**：全卷积网络的核心思想是在卷积网格上对激活值取平均。这种网络通常包含多层卷积，其中最后几层会采用步幅为2的卷积，随后接一个自适应平均池化层、一个用于去除单位轴的展平层，最后是线性层。较大的批次由于基于更多数据计算梯度，因此梯度更为准确。然而，更大的批次意味着每个 epoch 中的批次数量减少，从而减少了模型更新权重的机会。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书中的内容。在此期间，我学习了残差网络（ResNet）、卷积神经网络、步幅与填充、全卷积网络、自适应平均池化层、展平层、激活函数和矩阵乘法等相关主题。这里我通过截图展示了利用Fastai和PyTorch实现的数据准备和全卷积网络的过程。希望你能从中获得一些见解，并加以实践。同时，也建议你花些时间学习下文提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - 【残差网络】(https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bef8be5ae7ef.png)\n\n**300天数据之旅第255天！**\n- **全卷积网络**：全卷积网络的理念是在卷积网格上对激活值取平均。这种网络通常包含多层卷积，其中最后几层会采用步幅为2的卷积，随后接一个自适应平均池化层、一个用于去除单位轴的展平层，最后是线性层。较大的批次由于基于更多数据计算梯度，因此梯度更为准确。然而，更大的批次意味着每个 epoch 中的批次数量减少，从而减少了模型更新权重的机会。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书中的内容。在此过程中，我学习了全卷积神经网络、ResNet搭建、跳跃连接、恒等映射、SGD优化器、批量归一化层、可训练参数、真实身份路径、卷积神经网络、平均池化层等相关主题。这里我通过截图展示了利用Fastai和PyTorch实现的ResNet架构及跳跃连接的过程。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习下文提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - 【残差网络】(https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9cd077374b94.png)\n\n**300天数据之旅第256天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书中的内容。在此过程中，我学习了残差网络、ReLU激活函数、跳跃连接、训练更深的模型、神经网络损失景观、网络茎部、卷积层、最大池化层等相关主题。所谓“茎部”，指的是卷积神经网络的前几层，其结构与网络主体部分不同。这里我通过截图展示了利用Fastai和PyTorch实现的训练更深的模型和网络茎部的过程。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习下文提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - 【残差网络】(https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_58f378409f11.png)\n\n**300天数据之旅第257天！**\n- **瓶颈层**：瓶颈层采用三层卷积：开头和结尾各一个1×1卷积，中间一个3×3卷积。1×1卷积速度更快，便于在输入和输出端使用更多滤波器。这两个1×1卷积先减少通道数，再恢复通道数，形成所谓的“瓶颈”。整体效果是能够在相同时间内使用更多的滤波器。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书中的内容。在此过程中，我学习了网络茎部、残差网络架构、瓶颈层、卷积神经网络、渐进式调整尺寸等相关主题。这里我通过截图展示了利用Fastai和PyTorch实现的训练更深的网络和瓶颈层的过程。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习下文提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - 【残差网络】(https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F13.%20ResNets\u002FResNets.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_47403290a305.png)\n\n**300天数据之旅第258天！**\n- **分割函数**：分割函数是告诉fastai库如何将模型拆分为参数组的函数，这些参数组在迁移学习过程中仅用于训练模型的头部部分。`params`函数则简单地返回给定模块的所有参数。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我了解了网络的主体与头部、批归一化层、U-Net学习器及其架构、生成式视觉模型、最近邻插值、转置卷积、暹罗网络、损失函数以及分割函数等相关内容。我在截图中展示了使用Fastai和PyTorch实现的暹罗网络模型、损失函数和分割函数。希望你能从中获得一些启发，并进一步深入研究。也建议花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - [**架构细节**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F14.%20Architecture%20Details\u002FArchitectures.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_634922ed5cb8.png)\n\n**300天数据之旅第259天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了随机梯度下降、损失函数、权重更新、优化函数、创建数据块和数据加载器、ResNet模型及学习器、训练过程等相关内容。我在截图中展示了使用Fastai和PyTorch准备数据集和构建基线模型的实现。希望你能从中获得一些见解，并继续深入探索。也建议花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - [**训练过程**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7fa5a0d8854e.png)\n\n**300天数据之旅第260天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了训练过程、随机梯度下降、优化函数、学习率查找器、动量、优化器回调、梯度清零、偏函数等相关内容。我在截图中展示了用于优化器和SGD的函数实现。希望你能从中获得一些启发，并继续深入研究。也建议花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - [**训练过程**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_94a317fdd4c7.png)\n\n**300天数据之旅第261天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了随机梯度下降和优化函数、动量、指数加权移动平均、梯度平均、回调、RMSProp、自适应学习率、发散与ε等相关内容。我在截图中展示了使用Fastai和PyTorch实现的动量和RMSProp。希望你能从中获得一些见解，并继续深入研究。也建议花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - [**训练过程**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_82c11a201b3f.png)\n\n**300天数据之旅第262天！**\n- **Adam优化器**：Adam结合了带有动量的SGD和RMSProp的思想，它利用梯度的移动平均作为方向，并除以梯度平方的移动平均的平方根，从而为每个参数提供自适应的学习率。它采用无偏移的移动平均。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了RMSProp优化器、SGD、Adam优化器、梯度的无偏移移动平均、动量参数、解耦权重衰减、L1和L2正则化、回调等相关内容。我在截图中展示了使用Fastai和PyTorch实现的RMSProp和Adam优化器。希望你能从中获得一些启发，并继续深入研究。也建议花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - [**训练过程**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_588e1b7772b6.png)\n\n**300天数据之旅第263天！**\n- **Adam优化器**：Adam结合了带有动量的SGD和RMSProp的思想，它利用梯度的移动平均作为方向，并除以梯度平方的移动平均的平方根，从而为每个参数提供自适应的学习率。它采用无偏移的移动平均。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了创建回调、损失函数、模型重置回调、RNN正则化、回调顺序与异常处理、随机梯度下降等相关内容。我在截图中展示了使用Fastai和PyTorch实现的模型重置回调和RNN正则化回调。希望你能从中获得一些见解，并继续深入研究。也建议花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch进行编码的深度学习》\n  - [**训练过程**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F15.%20Training%20Process\u002FTraining.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8375c2844509.png)\n\n**300天数据之旅第264天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书的内容。在这里，我学习了神经网络、从头构建神经网络、模拟一个神经元、非线性激活函数、隐藏层大小、全连接层和密集层、线性层、从零开始实现矩阵乘法、逐元素运算等主题。我在截图中展示了使用Fastai和PyTorch实现的从零开始的矩阵乘法和逐元素运算。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下面提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_ff71ca4fda31.png)\n\n**300天数据之旅第265天！**\n- **前向传播与反向传播**：计算给定损失对其参数的所有梯度被称为反向传播。同样，根据矩阵乘积计算模型在给定输入上的输出则称为前向传播。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书的内容。在这里，我学习了标量广播、向量与矩阵的广播、unsqueeze方法、爱因斯坦求和约定、矩阵乘法、前向与反向传播、定义和初始化层、激活函数、线性层、权重与偏置等主题。我在截图中展示了使用Fastai和PyTorch实现的爱因斯坦求和约定以及线性层的定义和初始化。希望你能从中获得一些见解，并加以实践。也希望大家能花些时间学习下面提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_67051f112dd7.png)\n\n**300天数据之旅第266天！**\n- **前向传播与反向传播**：计算给定损失对其参数的所有梯度被称为反向传播。同样，根据矩阵乘积计算模型在给定输入上的输出则称为前向传播。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书的内容。在这里，我学习了均值与标准差、矩阵乘法、Xavier初始化、ReLU激活函数、Kaiming初始化、权重与激活等主题。我在截图中展示了使用Fastai和PyTorch实现的Xavier初始化、ReLU激活函数和矩阵乘法。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习下面提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7229ca6219fd.png)\n\n**300天数据之旅第267天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书的内容。在这里，我学习了Kaiming初始化、前向传播、均方误差损失函数、梯度与反向传播、线性层与ReLU激活函数、链式法则、反向传播等主题。我在截图中展示了使用Fastai和PyTorch实现的Kaiming初始化、MSE损失函数和梯度。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习下面提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b040f828be3f.png)\n\n**300天数据之旅第268天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书的内容。在这里，我学习了矩阵乘法的梯度、符号计算、前向与反向传播函数、模型参数、权重与偏置、模型重构、可调用模块等主题。我在截图中展示了使用Fastai和PyTorch实现的ReLU模块、线性模块和均方误差模块。希望你能从中获得一些启发，并加以实践。也希望大家能花些时间学习下面提到的书籍中的内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0c598302629f.png)\n\n**300天数据之旅第269天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了模型架构的初始化、可调用函数、前向与反向传播函数、线性函数、均方误差损失函数、ReLU激活函数、反向传播函数与梯度、Squeeze函数等主题。此外，我还了解了扰动与神经网络、梯度消失问题以及卷积神经网络。我在截图中展示了使用Fastai和PyTorch实现定义模型架构、层函数和ReLU的过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_629b0347bb0d.png)\n\n**300天数据之旅第270天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了定义基类和子类、线性层、ReLU激活函数及非线性变换、均方误差函数、超类初始化器、Kaiming初始化、逐元素算术运算与广播等主题。我在截图中展示了使用Fastai和PyTorch实现定义线性层和线性模型的过程。希望你能从中获得一些见解，并进一步实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**神经网络基础**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F16.%20Neural%20Network%20Foundations\u002FNeuralFoundations.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_9aea492b6620.png)\n\n**300天数据之旅第271天！**\n- **类别激活图**：类别激活图利用平均池化层之前的最后一层卷积层的输出与预测结果，生成模型决策的热力图可视化。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了CNN解释、类别激活图、钩子、热力图可视化、激活值与卷积层、点积、特征图、数据加载器等相关主题。我在截图中展示了使用Fastai和PyTorch实现定义钩子函数和解码图像的过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**带有CAM的CNN解释**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F17.%20CNN%20Interpretation\u002FCNN%20Interpretation.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_21b7a54e88e0.png)\n\n**300天数据之旅第272天！**\n- **类别激活图**：类别激活图利用平均池化层之前的最后一层卷积层的输出与预测结果，生成模型决策的热力图可视化。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了钩子类与上下文管理器、梯度类别激活图、热力图可视化、激活值与权重、梯度与反向传播、模型解释等相关主题。我在截图中展示了使用Fastai和PyTorch实现定义钩子函数、激活值、梯度及热力图可视化的过程。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**带有CAM的CNN解释**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F17.%20CNN%20Interpretation\u002FCNN%20Interpretation.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_52c585b7f095.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_89fa67898db1.png)\n\n**300天数据之旅第273天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了从零开始构建Fastai Learner、因变量与自变量、词汇表、数据集与索引等相关主题。我还了解了卷积神经网络、扰动和损失函数。我在截图中展示了使用Fastai和PyTorch准备训练集和验证集的过程。希望你能从中获得一些启发，并加以实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始的Fastai Learner**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e44c2a2d6fb4.png)\n\n**300天数据之旅第274天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在此期间，我学习了创建批处理函数、并行预处理、解码图像、数据加载器类、归一化与图像统计信息、调整轴顺序、精度等相关主题。我在截图中展示了使用Fastai和PyTorch实现数据加载器初始化与归一化的过程。希望你能从中获得一些见解，并加以实践。也建议你花些时间学习下方提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始的Fastai Learner**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d8c6ca7d068e.png)\n\n**300天数据之旅第275天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书。在这里，我学习了模块与参数、前向传播函数、卷积层、训练属性、Kaiming归一化和Xavier归一化初始化器、变换函数、权重与偏置、线性模型、张量等主题。我在截图中展示了如何使用Fastai和PyTorch定义模块：卷积层和线性模型的实现。希望你能从中获得一些启发，并进一步实践。也希望大家能花些时间学习下面提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始的Fastai学习者**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_17784d8ad43f.png)\n\n**300天数据之旅第276天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书。在这里，我学习了卷积神经网络、线性模型、测试模块、序列模块、参数、自适应池化层及均值、步幅、钩子函数、流水线等主题。我在截图中展示了如何使用Fastai和PyTorch实现测试模块、序列模块和卷积神经网络。希望你能从中获得一些 insights，并加以实践。也希望大家能花些时间学习下面提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始的Fastai学习者**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_98be4032e100.png)\n\n**300天数据之旅第277天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书。在这里，我学习了损失函数、负对数似然函数、Log Softmax函数、指数之和的对数、随机梯度下降优化器函数、数据加载器、训练集和验证集等主题。我在截图中展示了如何使用Fastai和PyTorch实现负对数似然函数、交叉熵损失函数、SGD优化器和数据加载器。希望你能从中获得一些 insights，并加以实践。也希望大家能花些时间学习下面提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始的Fastai学习者**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_b523a3415add.png)\n\n**300天数据之旅第278天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书。在这里，我学习了数据、卷积神经网络模型、损失函数、随机梯度下降与优化函数、学习者、回调函数、参数、训练与轮次等主题。我在截图中展示了如何使用Fastai和PyTorch实现学习者和回调函数。希望你能从中获得一些 insights，并加以实践。也希望大家能花些时间学习下面提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**从零开始的Fastai学习者**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F18.%20Fastai%20Learner\u002FFastai%20Learner.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_df947f01fade.png)\n\n**300天数据之旅第279天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书。在这里，我学习了二分类、胸部X光片、DICOM（医学数字成像与通信标准）、绘制DICOM数据、随机分割函数、医学影像、像素数据等主题。我在截图中展示了如何使用Fastai和PyTorch获取DICOM文件并进行检查。希望你能从中获得一些 insights，并加以实践。也希望大家能花些时间学习下面提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**胸部X光片分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_1d770d10214c.png)\n\n**300天数据之旅第280天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》这本书。在这里，我学习了二分类、数据块和数据加载器的初始化、图像块与类别块、批量变换、预训练模型的训练、学习率查找器、张量与概率、模型解释等主题。我在截图中展示了如何使用Fastai和PyTorch实现数据块和数据加载器的初始化、预训练模型的训练以及模型解释。希望你能从中获得一些 insights，并加以实践。也希望大家能花些时间学习下面提到的书籍内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [**胸部X光片分类**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4f034a80f559.png)\n\n**300天数据之旅第281天！**\n- **灵敏度与特异性**：灵敏度 = 真阳性 \u002F（真阳性 + 假阴性）。它也被称为第二类错误。特异性 = 真阴性 \u002F（假阳性 + 真阴性）。它也被称为第一类错误。在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch的编码者深度学习》一书的内容。在这里，我学习了灵敏度与特异性、阳性预测值与阴性预测值、混淆矩阵与模型解释、第一类与第二类错误、准确率与患病率等主题。我在截图中展示了使用Fastai和PyTorch实现的混淆矩阵、灵敏度与特异性以及准确率。希望你能从中获得一些启发，并进一步深入研究。我也希望你能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - 【胸部X光片分类】（https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F19.%20Chest%20XRays%20Classification\u002FXRays%20Classification.ipynb）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a3194c82f68a.png)\n\n**300天数据之旅第282天！**\n- **交叉验证**：交叉验证是构建机器学习模型过程中的一个步骤，它帮助我们确保模型能够准确地拟合数据，同时避免过拟合。在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》一书的内容。在这里，我学习了监督学习与无监督学习、特征、样本与目标、分类与回归、聚类、t分布随机邻域嵌入、二维数组、交叉验证、过拟合等主题。我在截图中展示了TSNE降维和数据集准备的实现。希望你能从中获得一些见解，并加以实践。我也希望你能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - 【监督学习与无监督学习】（https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_d17cefc9f311.png)\n\n**300天数据之旅第283天！**\n- **交叉验证**：交叉验证是构建机器学习模型过程中的一个步骤，它帮助我们确保模型能够准确地拟合数据，同时避免过拟合。在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》一书的内容。在这里，我学习了决策树与分类、特征与参数、准确率与模型预测、过拟合与模型泛化、训练损失与验证损失、交叉验证等主题。我在截图中展示了决策树分类器和模型评估的实现。希望你能从中获得一些见解，并加以实践。我也希望你能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - 【监督学习与无监督学习】（https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_fe41dc14f6d1.png)\n\n**300天数据之旅第284天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》一书的内容。在这里，我学习了分层K折交叉验证、偏斜数据集与分类、数据分布、留出法交叉验证、时间序列数据、回归与斯特格斯法则、概率、评估指标与准确率等主题。我在截图中展示了标签分布与分层K折交叉验证的实现。希望你能从中获得一些见解，并加以实践。我也希望你能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - 【监督学习与无监督学习】（https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F01.%20Supervised%20Unsupervised%20Learning\u002FSupervised%20Unsupervised.ipynb）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_15b97af623a0.png)\n\n**300天数据之旅第285天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》一书的内容。在这里，我学习了评估指标与准确率分数、训练集与验证集、精确率与召回率、真正例与真负例、假正例与假负例、二分类等主题。我在截图中展示了真负例、假负例、假正例以及准确率分数的实现。希望你能从中获得一些见解，并加以实践。我也希望你能花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - 【评估指标】（https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb）\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8c8b49cd3cc4.png)\n\n**300天数据之旅第286天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》这本书的内容。在这里，我学习了真正例率、召回率与灵敏度、假正例率与特异性、ROC曲线下面积、预测、概率与阈值、对数损失函数、多分类以及宏平均精确率等主题。我在截图中展示了真负例率、假正例率、对数损失函数和宏平均精确率的实现。希望你能从中获得一些见解，并进一步深入研究。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [评估指标](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_8a727a6a3779.png)\n\n**300天数据之旅第287天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》这本书的内容。在这里，我学习了多分类、宏平均精确率、微平均精确率、加权精确率、召回指标、随机森林回归器、均方误差、均方根误差等主题。我在截图中展示了微平均精确率和加权精确率的实现。希望你能从中获得一些见解，并继续探索。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [评估指标](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_bf1fa3ebcd81.png)\n\n**300天数据之旅第288天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》这本书的内容。在这里，我学习了多分类的召回指标、加权F1分数、混淆矩阵、第一类错误和第二类错误、AUC曲线、多标签分类以及平均精确度等主题。我在截图中展示了加权F1分数和平均精确率的实现。希望你能从中获得一些见解，并继续深入研究。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [评估指标](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7ca73029e358.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_aabe94436e8a.png)\n\n**300天数据之旅第289天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《接近几乎任何机器学习问题》这本书的内容。在这里，我学习了回归评估指标，如平均绝对误差和平均误差、均方根误差、对数平方误差、平均绝对百分比误差、R²和决定系数、Cohen's Kappa评分、MCC评分等主题。我在截图中展示了平均绝对误差和平均误差、对数平方误差、平均绝对百分比误差、R²和MCC评分的实现。希望你能从中获得一些见解，并继续深入研究。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 书籍：\n  - 《使用Fastai和PyTorch的编码者深度学习》\n  - [评估指标](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FApproachingAnyMachineLearning\u002Fblob\u002Fmain\u002F02.%20Evaluation%20Metrics\u002FEvaluation%20Metrics.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_3ba52741364b.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_87822b256ae1.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a1fd4e325efc.png)\n\n**300天数据之旅第290天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了目标检测与微调、图像分割、张量与宽高比、数组、数据集和数据加载器等内容。我还开始学习Coursera上的“面向生产的机器学习工程”专项课程。在这里，我学习了机器学习项目的步骤与案例研究、机器学习项目生命周期等主题。我在截图中展示了数据集类的实现。希望你能从中获得一些见解，并继续深入研究。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 资源：\n  - 《面向生产的机器学习工程》\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a80af5e3faa7.png)\n\n**300天数据之旅第291天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客的内容。在这里，我学习了OpenCV、图像的加载与显示、像素访问、数组切片与裁剪、图像缩放与旋转、图像平滑、图像绘制等主题。此外，我还学习了Coursera“面向生产的机器学习工程”专项课程中的机器学习项目生命周期、部署模式和管道监控等内容。我在截图中展示了使用OpenCV进行图像缩放与旋转、图像平滑以及图像绘制的实现。希望你能从中获得一些见解，并继续深入研究。也建议你花些时间学习下方提到的书籍中的相关内容。对未来几天充满期待！！\n- 资源：\n  - 《面向生产的机器学习工程》\n  - [PyImageSearch](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [OpenCV笔记本](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOpenCV.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_82d28dd3b5bf.png)\n\n**300天数据之旅第292天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客中的内容。在这里，我学习了OpenCV、物体计数、图像灰度化、边缘检测、阈值分割、轮廓检测与绘制、腐蚀与膨胀、掩码与位运算等主题。此外，我还阅读了Coursera“面向生产的机器学习工程”专项课程中的建模概述、关键挑战以及低平均误差等内容。在截图中，我展示了使用OpenCV实现的图像灰度化、边缘检测、阈值分割、轮廓检测与绘制、腐蚀与膨胀等操作。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 资源：\n  - **面向生产的机器学习工程**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**OpenCV笔记本**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOpenCV.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_4f61b6ea52e8.png)\n\n**300天数据之旅第293天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客中的内容。在这里，我学习了OpenCV、图像旋转、图像预处理、旋转矩阵与中心坐标、图像解析、边缘检测与轮廓检测、图像掩码与模糊处理等主题。此外，我还阅读了Coursera“面向生产的机器学习工程”专项课程中的基准模型、模型选择与训练、误差分析与优先级排序等内容。在截图中，我展示了使用OpenCV实现的图像旋转以及获取图像感兴趣区域的操作。希望你能从中获得一些 insights，并进一步实践。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 资源：\n  - **面向生产的机器学习工程**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**OpenCV项目I**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20I.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a720268f39cc.png)\n\n**300天数据之旅第294天！**\n- **直方图匹配**：直方图匹配可以作为一种归一化技术，用于图像处理流水线中进行颜色校正和颜色匹配，从而在光照条件变化时仍能获得一致且归一化的图像表示。在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客中的内容。在这里，我学习了OpenCV、颜色检测、RGB色彩空间、直方图匹配、像素分布、累积分布、图像缩放等主题。此外，我还阅读了Coursera“面向生产的机器学习工程”专项课程中的偏斜数据集、性能审计、以数据为中心的人工智能开发以及数据增强等内容。在截图中，我展示了使用OpenCV实现的直方图匹配操作。希望你能从中获得一些 insights，并加以实践。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 资源：\n  - **面向生产的机器学习工程**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**OpenCV项目II**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F01.%20OpenCV\u002FOCV%20Project%20II.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_69ee5757b2e7.png)\n\n**300天数据之旅第295天！**\n- **直方图匹配**：直方图匹配可以作为一种归一化技术，用于图像处理流水线中进行颜色校正和颜色匹配，从而在光照条件变化时仍能获得一致且归一化的图像表示。在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客中的内容。在这里，我学习了卷积神经网络、卷积矩阵、卷积核、空间维度、填充、图像感兴趣区域、逐元素乘法与加法、强度重缩放、拉普拉斯核、模糊检测与平滑处理等主题。在截图中，我展示了卷积方法的实现以及卷积核的构建过程。希望你能从中获得一些 insights，并继续深入研究。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 资源：\n  - **面向生产的机器学习工程**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**卷积**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetwork\u002FConvolutions.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_c20efe883895.png)\n\n**300天数据之旅第296天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客中的内容。在这里，我学习了卷积层、滤波器与卷积核大小、步幅、填充、输入数据格式、扩张率、激活函数、权重与偏置、卷积核与偏置的初始化与正则化、泛化与过拟合、卷积核与偏置的约束、加州理工学院数据集、带步幅的网络等主题。在截图中，我展示了带步幅的网络的实现。希望你能从中获得一些 insights，并继续探索。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 资源：\n  - **面向生产的机器学习工程**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**卷积层**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb)\n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f504525f28d3.png)\n\n**300天数据之旅第297天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客中的内容。在这里，我学习了卷积神经网络架构、步幅网络、标签二值化与独热编码、图像数据生成器与数据增强、图像加载与调整大小等主题。我在截图中展示了标签二值化和数据集准备的实现过程。希望你能从中获得一些启发，并加以实践。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 资源：\n  - **生产环境中的机器学习工程**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**卷积层**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb) \n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_15a5cfb8ebb1.png)\n\n**300天数据之旅第298天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了PyImageSearch博客中的内容。在这里，我学习了卷积神经网络、Adam优化函数、编译与训练步幅网络模型、数据增强与图像数据生成器、分类报告、绘制训练损失与准确率曲线、过拟合与泛化等主题。我在截图中展示了模型编译与训练、分类报告以及训练损失和准确率的实现过程。希望你能从中获得一些见解，并进一步实践。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 资源：\n  - **生产环境中的机器学习工程**\n  - [**PyImageSearch**](https:\u002F\u002Fwww.pyimagesearch.com\u002F)\n  - [**卷积层**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FComputerVision\u002Fblob\u002Fmain\u002F02.%20ConvolutionalNeuralNetworks\u002FConvolutional%20Layers.ipynb) \n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_f6a94a9d6873.png)\n\n**300天数据之旅第299天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了Transformer模型、GPT-2预训练模型与分词器、编码与解码方法、数据集准备、Transform方法、数据加载器等主题。我在截图中展示了使用Fastai和PyTorch实现的预训练GPT-2模型与分词器，以及转换后的数据加载器。希望你能从中获得一些启发，并继续深入学习。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - **使用Fastai和PyTorch进行编码的深度学习**\n  - [**Transformer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F20.%20Transformers\u002FTransformers.ipynb) \n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_7a17499df894.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_0580b9ce0eaa.png)\n\n**300天数据之旅第300天！**\n- 在我的机器学习和深度学习之旅中，我阅读并实践了《使用Fastai和PyTorch进行编码的深度学习》一书的内容。在这里，我学习了Transformer模型、数据加载器、批次大小与序列长度、语言模型、GPT-2模型的微调、回调函数、学习者对象、困惑度与交叉熵损失函数、学习率查找器、训练与生成预测等内容。我在截图中展示了使用Fastai和PyTorch初始化数据加载器、微调GPT-2模型以及使用学习率查找器的过程。希望你能从中获得一些见解，并继续深入学习。同时，也建议你花些时间学习下方提到的书籍中的相关内容。对接下来的日子充满期待！！\n- 书籍：\n  - **使用Fastai和PyTorch进行编码的深度学习**\n  - [**Transformer**](https:\u002F\u002Fgithub.com\u002FThinamXx\u002FFastai\u002Fblob\u002Fmain\u002F20.%20Transformers\u002FTransformers.ipynb) \n\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_a6ff45d24d63.png)\n![图片](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_readme_e3bb239dab3a.png)","# 300Days__MachineLearningDeepLearning 快速上手指南\n\n本项目并非一个单一的 Python 库，而是一个为期 300 天的机器学习与深度学习学习路径记录。它包含了精选的书单、研究论文以及大量基于 **Python**、**PyTorch**、**Fastai** 和 **Scikit-Learn** 的实战代码笔记本（Notebooks）。\n\n本指南将帮助你搭建运行这些项目所需的环境，并启动第一个学习示例。\n\n## 环境准备\n\n在开始之前，请确保你的系统满足以下要求：\n\n*   **操作系统**: Windows, macOS 或 Linux\n*   **Python 版本**: 推荐 Python 3.8 - 3.10\n*   **核心依赖**:\n    *   PyTorch (深度学习框架)\n    *   Fastai (高层深度学习库)\n    *   Scikit-Learn (传统机器学习)\n    *   Jupyter Lab \u002F Notebook (用于运行 `.ipynb` 文件)\n    *   OpenCV (计算机视觉任务)\n    *   Pandas, NumPy, Matplotlib (数据处理与可视化)\n\n> **国内加速建议**：\n> 推荐使用清华源或阿里源安装 Python 包，以显著提升下载速度。\n> 配置临时镜像源方法：在 `pip` 命令后添加 `-i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple`\n\n## 安装步骤\n\n### 1. 克隆项目代码\n首先，将仓库克隆到本地：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FThinamXx\u002F300Days__MachineLearningDeepLearning.git\ncd 300Days__MachineLearningDeepLearning\n```\n\n### 2. 创建虚拟环境\n建议使用 `conda` 或 `venv` 创建隔离环境。\n\n**使用 Conda (推荐):**\n```bash\nconda create -n ml300days python=3.9\nconda activate ml300days\n```\n\n**使用 Venv:**\n```bash\npython -m venv ml300days\n# Windows\nml300days\\Scripts\\activate\n# macOS\u002FLinux\nsource ml300days\u002Fbin\u002Factivate\n```\n\n### 3. 安装依赖库\n由于项目涵盖面广（从基础回归到 GAN 和 NLP），建议安装包含主要深度学习框架的综合环境。\n\n**安装 PyTorch (使用清华源加速):**\n```bash\npip install torch torchvision torchaudio -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n**安装其他核心依赖:**\n```bash\npip install fastai scikit-learn opencv-python jupyterlab pandas numpy matplotlib seaborn -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n## 基本使用\n\n本项目的主要使用方式是阅读并运行 `Projects and Notebooks` 目录下的 Jupyter Notebook 文件。这些文件按主题分类（如逻辑回归、LeNet 架构、情感分析等）。\n\n### 启动 Jupyter Lab\n在项目根目录下运行：\n\n```bash\njupyter lab\n```\n\n### 运行第一个示例：从零实现逻辑回归\n根据 README 中的 \"Day 1\" 和 \"Day 2\" 记录，建议从基础算法开始。\n\n1.  在 Jupyter Lab 界面中，导航至以下路径（对应项目列表第 2 项）：\n    `MachineLearning__Algorithms\u002FLogisticRegression\u002FLogisticRegression.ipynb`\n    *(注：如果本地目录结构与链接略有不同，请在克隆后的文件夹中搜索 `LogisticRegression.ipynb`)*\n\n2.  打开该 `.ipynb` 文件。\n\n3.  点击菜单栏的 **Kernel** -> **Restart & Run All**，即可从头到尾执行代码，观察梯度下降和交叉验证的实现过程及可视化结果。\n\n### 进阶示例：使用 Fastai 进行图像分类\n如果你想直接尝试深度学习项目（对应项目列表第 17 项）：\n\n1.  导航至 `Fastai\u002F4. Image Classification\u002FImageClassification.ipynb`。\n2.  打开并运行所有单元格。\n3.  该脚本会自动下载数据集（如 Imagenette），训练一个图像分类模型并评估准确率。\n\n通过依次运行这些 Notebook，你可以完整复现作者 300 天的学习与实战历程。","一名刚转行数据科学的工程师，正试图在三个月内从零掌握机器学习与深度学习核心技能，以应对公司新启动的图像识别项目。\n\n### 没有 300Days__MachineLearningDeepLearning 时\n- **学习路径混乱**：面对海量的经典教材（如《动手学深度学习》、《Speech and Language Processing》）和论文，不知从何入手，容易在理论海洋中迷失方向。\n- **理论与实践脱节**：读懂了逻辑回归或 LeNet 架构的数学公式，却缺乏从零手写代码的实现参考，导致无法真正理解算法底层逻辑。\n- **项目落地困难**：在处理具体任务（如情感分析、犬种分类）时，找不到涵盖数据预处理、模型构建到调优的完整 Notebook 范例，反复踩坑。\n- **技术栈覆盖不全**：难以系统性地同时掌握 PyTorch、Fastai、Keras 等多个主流框架的最佳实践，知识体系支离破碎。\n\n### 使用 300Days__MachineLearningDeepLearning 后\n- **路线清晰高效**：直接跟随作者验证过的\"300 天”书单与完成状态，按部就班地攻克从基础机器学习到 BERT、GAN 等前沿模型的学习关卡。\n- **代码级深度理解**：参考“从零实现逻辑回归”和\"LeNet 架构复现”等源码，将抽象理论转化为可运行的代码，彻底吃透算法原理。\n- **场景化快速复用**：利用现成的 CIFAR10 物体识别、RNN\u002FCNN 情感分析及自然语言推断项目笔记，快速迁移解决公司业务中的类似痛点。\n- **全框架实战能力**：通过 Fastai 系列教程（如熊检测器、数字分类器），迅速掌握多框架下的模型生产与部署流程，提升工程化水平。\n\n300Days__MachineLearningDeepLearning 不仅是一份资源清单，更是一条经过实战验证的、从理论入门到项目落地的系统化成长捷径。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FThinamXx_300Days__MachineLearningDeepLearning_176f2b74.png","ThinamXx","Thinam Tamang","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FThinamXx_d3b8e053.jpg","AI @ Speechify 🚀","Speechify","Kathmandu, Nepal",null,"https:\u002F\u002Flinktr.ee\u002FThinam","https:\u002F\u002Fgithub.com\u002FThinamXx",583,170,"2026-04-01T18:40:59","MIT","","未说明",{"notes":88,"python":86,"dependencies":89},"该项目是一个包含 300 天机器学习与深度学习学习路径的资源汇总仓库，主要提供书籍阅读清单、研究论文链接以及多个独立项目（如房价预测、情感分析、图像分类等）的 Notebook 代码实现。README 中未明确列出具体的运行环境配置、依赖版本或硬件要求。用户需根据各个子项目（如 Fastai、PyTorch、TensorFlow 相关笔记）的具体代码内容自行安装对应的 Python 库和配置环境。建议参考项目中提到的经典教材（如《Hands On Machine Learning》、《Dive into Deep Learning》）的环境设置指南。",[90,91,92,93,94,95,96],"scikit-learn","tensorflow","keras","pytorch","fastai","opencv-python","transformers",[14],[99,100,101],"machine-learning","deep-learning","python","2026-03-27T02:49:30.150509","2026-04-09T12:33:20.809834",[],[]]