[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-donnemartin--data-science-ipython-notebooks":3,"tool-donnemartin--data-science-ipython-notebooks":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",150037,2,"2026-04-10T23:33:47",[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。",108322,"2026-04-10T11:39:34",[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},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"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],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":77,"owner_location":78,"owner_email":79,"owner_twitter":80,"owner_website":81,"owner_url":82,"languages":83,"stars":98,"forks":99,"last_commit_at":100,"license":101,"difficulty_score":32,"env_os":102,"env_gpu":103,"env_ram":102,"env_deps":104,"category_tags":117,"github_topics":119,"view_count":130,"oss_zip_url":80,"oss_zip_packed_at":80,"status":17,"created_at":131,"updated_at":132,"faqs":133,"releases":169},4992,"donnemartin\u002Fdata-science-ipython-notebooks","data-science-ipython-notebooks","Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.","data-science-ipython-notebooks 是一个专为数据科学爱好者打造的开源学习资源库，汇集了丰富的 Python Jupyter Notebook 实战教程。它旨在解决初学者和从业者在掌握复杂数据技术时面临的“理论脱离实践”痛点，通过提供可运行、可修改的代码示例，帮助用户快速上手从基础数据处理到前沿深度学习的全流程技能。\n\n这套教程非常适合数据科学家、机器学习工程师、研究人员以及希望转行进入数据领域的开发者使用。无论是需要复习 Python 基础、精通 pandas 与 NumPy 进行数据分析，还是渴望深入探索 TensorFlow、Keras、Theano 等框架下的深度学习模型（如卷积神经网络、循环神经网络），都能在这里找到对应的实操指南。此外，它还涵盖了 Scikit-learn 机器学习、Spark 大数据处理以及 AWS 云服务应用等关键主题。\n\n其独特的技术亮点在于内容覆盖面极广且结构清晰，不仅包含了经典的统计推断与可视化（matplotlib）教学，还整合了 Kaggle 竞赛案例与商业分析实战，甚至提供了 Hadoop MapReduce 等传统","data-science-ipython-notebooks 是一个专为数据科学爱好者打造的开源学习资源库，汇集了丰富的 Python Jupyter Notebook 实战教程。它旨在解决初学者和从业者在掌握复杂数据技术时面临的“理论脱离实践”痛点，通过提供可运行、可修改的代码示例，帮助用户快速上手从基础数据处理到前沿深度学习的全流程技能。\n\n这套教程非常适合数据科学家、机器学习工程师、研究人员以及希望转行进入数据领域的开发者使用。无论是需要复习 Python 基础、精通 pandas 与 NumPy 进行数据分析，还是渴望深入探索 TensorFlow、Keras、Theano 等框架下的深度学习模型（如卷积神经网络、循环神经网络），都能在这里找到对应的实操指南。此外，它还涵盖了 Scikit-learn 机器学习、Spark 大数据处理以及 AWS 云服务应用等关键主题。\n\n其独特的技术亮点在于内容覆盖面极广且结构清晰，不仅包含了经典的统计推断与可视化（matplotlib）教学，还整合了 Kaggle 竞赛案例与商业分析实战，甚至提供了 Hadoop MapReduce 等传统大数据技术的 Python 实现方案。所有笔记均以交互式形式呈现，让用户能在阅读代码的同时立即验证结果，极大地降低了学习门槛，是构建系统化数据科学知识体系的理想伴侣。","\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_95f950761158.gif\">\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_8f54cd3c93b2.png\">\n  \u003Cbr\u002F>\n\u003C\u002Fp>\n\n# data-science-ipython-notebooks\n\n## Index\n\n* [deep-learning](#deep-learning)\n    * [tensorflow](#tensor-flow-tutorials)\n    * [theano](#theano-tutorials)\n    * [keras](#keras-tutorials)\n    * [caffe](#deep-learning-misc)\n* [scikit-learn](#scikit-learn)\n* [statistical-inference-scipy](#statistical-inference-scipy)\n* [pandas](#pandas)\n* [matplotlib](#matplotlib)\n* [numpy](#numpy)\n* [python-data](#python-data)\n* [kaggle-and-business-analyses](#kaggle-and-business-analyses)\n* [spark](#spark)\n* [mapreduce-python](#mapreduce-python)\n* [amazon web services](#aws)\n* [command lines](#commands)\n* [misc](#misc)\n* [notebook-installation](#notebook-installation)\n* [credits](#credits)\n* [contributing](#contributing)\n* [contact-info](#contact-info)\n* [license](#license)\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"http:\u002F\u002Fi.imgur.com\u002FZhKXrKZ.png\">\n\u003C\u002Fp>\n\n## deep-learning\n\nIPython Notebook(s) demonstrating deep learning functionality.\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_fef6d0bb0d1b.png\">\n\u003C\u002Fp>\n\n### tensor-flow-tutorials\n\nAdditional TensorFlow tutorials:\n\n* [pkmital\u002Ftensorflow_tutorials](https:\u002F\u002Fgithub.com\u002Fpkmital\u002Ftensorflow_tutorials)\n* [nlintz\u002FTensorFlow-Tutorials](https:\u002F\u002Fgithub.com\u002Fnlintz\u002FTensorFlow-Tutorials)\n* [alrojo\u002Ftensorflow-tutorial](https:\u002F\u002Fgithub.com\u002Falrojo\u002Ftensorflow-tutorial)\n* [BinRoot\u002FTensorFlow-Book](https:\u002F\u002Fgithub.com\u002FBinRoot\u002FTensorFlow-Book)\n* [tuanavu\u002Ftensorflow-basic-tutorials](https:\u002F\u002Fgithub.com\u002Ftuanavu\u002Ftensorflow-basic-tutorials)\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tsf-basics](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F1_intro\u002Fbasic_operations.ipynb) | Learn basic operations in TensorFlow, a library for various kinds of perceptual and language understanding tasks from Google. |\n| [tsf-linear](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F2_basic_classifiers\u002Flinear_regression.ipynb) | Implement linear regression in TensorFlow. |\n| [tsf-logistic](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F2_basic_classifiers\u002Flogistic_regression.ipynb) | Implement logistic regression in TensorFlow. |\n| [tsf-nn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F2_basic_classifiers\u002Fnearest_neighbor.ipynb) | Implement nearest neighboars in TensorFlow. |\n| [tsf-alex](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Falexnet.ipynb) | Implement AlexNet in TensorFlow. |\n| [tsf-cnn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Fconvolutional_network.ipynb) | Implement convolutional neural networks in TensorFlow. |\n| [tsf-mlp](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Fmultilayer_perceptron.ipynb) | Implement multilayer perceptrons in TensorFlow. |\n| [tsf-rnn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Frecurrent_network.ipynb) | Implement recurrent neural networks in TensorFlow. |\n| [tsf-gpu](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F4_multi_gpu\u002Fmultigpu_basics.ipynb) | Learn about basic multi-GPU computation in TensorFlow. |\n| [tsf-gviz](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F5_ui\u002Fgraph_visualization.ipynb) | Learn about graph visualization in TensorFlow. |\n| [tsf-lviz](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F5_ui\u002Floss_visualization.ipynb) | Learn about loss visualization in TensorFlow. |\n\n### tensor-flow-exercises\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tsf-not-mnist](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F1_notmnist.ipynb) | Learn simple data curation by creating a pickle with formatted datasets for training, development and testing in TensorFlow. |\n| [tsf-fully-connected](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F2_fullyconnected.ipynb) | Progressively train deeper and more accurate models using logistic regression and neural networks in TensorFlow. |\n| [tsf-regularization](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F3_regularization.ipynb) | Explore regularization techniques by training fully connected networks to classify notMNIST characters in TensorFlow. |\n| [tsf-convolutions](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F4_convolutions.ipynb) | Create convolutional neural networks in TensorFlow. |\n| [tsf-word2vec](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F5_word2vec.ipynb) | Train a skip-gram model over Text8 data in TensorFlow. |\n| [tsf-lstm](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F6_lstm.ipynb) | Train a LSTM character model over Text8 data in TensorFlow. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"http:\u002F\u002Fwww.deeplearning.net\u002Fsoftware\u002Ftheano\u002F_static\u002Ftheano_logo_allblue_200x46.png\">\n\u003C\u002Fp>\n\n### theano-tutorials\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [theano-intro](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Fintro_theano\u002Fintro_theano.ipynb) | Intro to Theano, which allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. |\n| [theano-scan](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Fscan_tutorial\u002Fscan_tutorial.ipynb) | Learn scans, a mechanism to perform loops in a Theano graph. |\n| [theano-logistic](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Fintro_theano\u002Flogistic_regression.ipynb) | Implement logistic regression in Theano. |\n| [theano-rnn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Frnn_tutorial\u002Fsimple_rnn.ipynb) | Implement recurrent neural networks in Theano. |\n| [theano-mlp](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Ftheano_mlp\u002Ftheano_mlp.ipynb) | Implement multilayer perceptrons in Theano. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"http:\u002F\u002Fi.imgur.com\u002FL45Q8c2.jpg\">\n\u003C\u002Fp>\n\n### keras-tutorials\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| keras | Keras is an open source neural network library written in Python. It is capable of running on top of either Tensorflow or Theano. |\n| [setup](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F0.%20Preamble.ipynb) | Learn about the tutorial goals and how to set up your Keras environment. |\n| [intro-deep-learning-ann](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.1%20Introduction%20-%20Deep%20Learning%20and%20ANN.ipynb) | Get an intro to deep learning with Keras and Artificial Neural Networks (ANN). |\n| [theano](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.2%20Introduction%20-%20Theano.ipynb) | Learn about Theano by working with weights matrices and gradients. |\n| [keras-otto](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.3%20Introduction%20-%20Keras.ipynb) | Learn about Keras by looking at the Kaggle Otto challenge. |\n| [ann-mnist](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.4%20%28Extra%29%20A%20Simple%20Implementation%20of%20ANN%20for%20MNIST.ipynb) | Review a simple implementation of ANN for MNIST using Keras. |\n| [conv-nets](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.1%20Supervised%20Learning%20-%20ConvNets.ipynb) | Learn about Convolutional Neural Networks (CNNs) with Keras. |\n| [conv-net-1](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.2.1%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20I.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 1. |\n| [conv-net-2](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.2.2%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20II.ipynb) | Recognize handwritten digits from MNIST using Keras - Part 2. |\n| [keras-models](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.3%20Supervised%20Learning%20-%20Famous%20Models%20with%20Keras.ipynb) | Use pre-trained models such as VGG16, VGG19, ResNet50, and Inception v3 with Keras. |\n| [auto-encoders](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F3.1%20Unsupervised%20Learning%20-%20AutoEncoders%20and%20Embeddings.ipynb) | Learn about Autoencoders with Keras. |\n| [rnn-lstm](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F3.2%20RNN%20and%20LSTM.ipynb) | Learn about Recurrent Neural Networks (RNNs) with Keras. |\n| [lstm-sentence-gen](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F3.3%20%28Extra%29%20LSTM%20for%20Sentence%20Generation.ipynb) |  Learn about RNNs using Long Short Term Memory (LSTM) networks with Keras. |\n\n### deep-learning-misc\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [deep-dream](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fdeep-dream\u002Fdream.ipynb) | Caffe-based computer vision program which uses a convolutional neural network to find and enhance patterns in images. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_1f65a7d5139b.png\">\n\u003C\u002Fp>\n\n## scikit-learn\n\nIPython Notebook(s) demonstrating scikit-learn functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [intro](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-intro.ipynb) | Intro notebook to scikit-learn.  Scikit-learn adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |\n| [knn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | Implement k-nearest neighbors in scikit-learn. |\n| [linear-reg](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-linear-reg.ipynb) | Implement linear regression in scikit-learn. |\n| [svm](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-svm.ipynb) | Implement support vector machine classifiers with and without kernels in scikit-learn. |\n| [random-forest](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-random-forest.ipynb) | Implement random forest classifiers and regressors in scikit-learn. |\n| [k-means](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-k-means.ipynb) | Implement k-means clustering in scikit-learn. |\n| [pca](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-pca.ipynb) | Implement principal component analysis in scikit-learn. |\n| [gmm](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-gmm.ipynb) | Implement Gaussian mixture models in scikit-learn. |\n| [validation](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-validation.ipynb) | Implement validation and model selection in scikit-learn. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_41fa0e3dc800.png\">\n\u003C\u002Fp>\n\n## statistical-inference-scipy\n\nIPython Notebook(s) demonstrating statistical inference with SciPy functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| scipy | SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. |\n| [effect-size](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscipy\u002Feffect_size.ipynb) | Explore statistics that quantify effect size by analyzing the difference in height between men and women.  Uses data from the Behavioral Risk Factor Surveillance System (BRFSS) to estimate the mean and standard deviation of height for adult women and men in the United States. |\n| [sampling](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscipy\u002Fsampling.ipynb) | Explore random sampling by analyzing the average weight of men and women in the United States using BRFSS data. |\n| [hypothesis](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscipy\u002Fhypothesis.ipynb) | Explore hypothesis testing by analyzing the difference of first-born babies compared with others. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_7ecca73c3570.png\">\n\u003C\u002Fp>\n\n## pandas\n\nIPython Notebook(s) demonstrating pandas functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [pandas](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002Fpandas.ipynb) | Software library written for data manipulation and analysis in Python. Offers data structures and operations for manipulating numerical tables and time series. |\n| [github-data-wrangling](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fviz\u002Fblob\u002Fmaster\u002Fgithubstats\u002Fdata_wrangling.ipynb) | Learn how to load, clean, merge, and feature engineer by analyzing GitHub data from the [`Viz`](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fviz) repo. |\n| [Introduction-to-Pandas](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.00-Introduction-to-Pandas.ipynb) | Introduction to Pandas. |\n| [Introducing-Pandas-Objects](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.01-Introducing-Pandas-Objects.ipynb) | Learn about Pandas objects. |\n| [Data Indexing and Selection](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.02-Data-Indexing-and-Selection.ipynb) | Learn about data indexing and selection in Pandas. |\n| [Operations-in-Pandas](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.03-Operations-in-Pandas.ipynb) | Learn about operating on data in Pandas. |\n| [Missing-Values](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.04-Missing-Values.ipynb) | Learn about handling missing data in Pandas. |\n| [Hierarchical-Indexing](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.05-Hierarchical-Indexing.ipynb) | Learn about hierarchical indexing in Pandas. |\n| [Concat-And-Append](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.06-Concat-And-Append.ipynb) | Learn about combining datasets: concat and append in Pandas. |\n| [Merge-and-Join](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.07-Merge-and-Join.ipynb) | Learn about combining datasets: merge and join in Pandas. |\n| [Aggregation-and-Grouping](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.08-Aggregation-and-Grouping.ipynb) | Learn about aggregation and grouping in Pandas. |\n| [Pivot-Tables](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.09-Pivot-Tables.ipynb) | Learn about pivot tables in Pandas. |\n| [Working-With-Strings](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.10-Working-With-Strings.ipynb) | Learn about vectorized string operations in Pandas. |\n| [Working-with-Time-Series](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.11-Working-with-Time-Series.ipynb) | Learn about working with time series in pandas. |\n| [Performance-Eval-and-Query](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.12-Performance-Eval-and-Query.ipynb) | Learn about high-performance Pandas: eval() and query() in Pandas. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_1f733496b99a.png\">\n\u003C\u002Fp>\n\n## matplotlib\n\nIPython Notebook(s) demonstrating matplotlib functionality.\n\n| Notebook | Description |\n|-----------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [matplotlib](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002Fmatplotlib.ipynb) | Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. |\n| [matplotlib-applied](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002Fmatplotlib-applied.ipynb) | Apply matplotlib visualizations to Kaggle competitions for exploratory data analysis.  Learn how to create bar plots, histograms, subplot2grid, normalized plots, scatter plots, subplots, and kernel density estimation plots. |\n| [Introduction-To-Matplotlib](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.00-Introduction-To-Matplotlib.ipynb) | Introduction to Matplotlib. |\n| [Simple-Line-Plots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.01-Simple-Line-Plots.ipynb) | Learn about simple line plots in Matplotlib. |\n| [Simple-Scatter-Plots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.02-Simple-Scatter-Plots.ipynb) | Learn about simple scatter plots in Matplotlib. |\n| [Errorbars.ipynb](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.03-Errorbars.ipynb) | Learn about visualizing errors in Matplotlib. |\n| [Density-and-Contour-Plots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.04-Density-and-Contour-Plots.ipynb) | Learn about density and contour plots in Matplotlib. |\n| [Histograms-and-Binnings](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.05-Histograms-and-Binnings.ipynb) | Learn about histograms, binnings, and density in Matplotlib. |\n| [Customizing-Legends](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.06-Customizing-Legends.ipynb) | Learn about customizing plot legends in Matplotlib. |\n| [Customizing-Colorbars](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.07-Customizing-Colorbars.ipynb) | Learn about customizing colorbars in Matplotlib. |\n| [Multiple-Subplots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.08-Multiple-Subplots.ipynb) | Learn about multiple subplots in Matplotlib. |\n| [Text-and-Annotation](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.09-Text-and-Annotation.ipynb) | Learn about text and annotation in Matplotlib. |\n| [Customizing-Ticks](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.10-Customizing-Ticks.ipynb) | Learn about customizing ticks in Matplotlib. |\n| [Settings-and-Stylesheets](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.11-Settings-and-Stylesheets.ipynb) | Learn about customizing Matplotlib: configurations and stylesheets. |\n| [Three-Dimensional-Plotting](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.12-Three-Dimensional-Plotting.ipynb) | Learn about three-dimensional plotting in Matplotlib. |\n| [Geographic-Data-With-Basemap](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.13-Geographic-Data-With-Basemap.ipynb) | Learn about geographic data with basemap in Matplotlib. |\n| [Visualization-With-Seaborn](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.14-Visualization-With-Seaborn.ipynb) | Learn about visualization with Seaborn. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_d8b18f24aab6.png\">\n\u003C\u002Fp>\n\n## numpy\n\nIPython Notebook(s) demonstrating NumPy functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [numpy](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002Fnumpy.ipynb) | Adds Python support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. |\n| [Introduction-to-NumPy](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.00-Introduction-to-NumPy.ipynb) | Introduction to NumPy. |\n| [Understanding-Data-Types](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.01-Understanding-Data-Types.ipynb) | Learn about data types in Python. |\n| [The-Basics-Of-NumPy-Arrays](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.02-The-Basics-Of-NumPy-Arrays.ipynb) | Learn about the basics of NumPy arrays. |\n| [Computation-on-arrays-ufuncs](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.03-Computation-on-arrays-ufuncs.ipynb) | Learn about computations on NumPy arrays: universal functions. |\n| [Computation-on-arrays-aggregates](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.04-Computation-on-arrays-aggregates.ipynb) | Learn about aggregations: min, max, and everything in between in NumPy. |\n| [Computation-on-arrays-broadcasting](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.05-Computation-on-arrays-broadcasting.ipynb) | Learn about computation on arrays: broadcasting in NumPy. |\n| [Boolean-Arrays-and-Masks](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.06-Boolean-Arrays-and-Masks.ipynb) | Learn about comparisons, masks, and boolean logic in NumPy. |\n| [Fancy-Indexing](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.07-Fancy-Indexing.ipynb) | Learn about fancy indexing in NumPy. |\n| [Sorting](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.08-Sorting.ipynb) | Learn about sorting arrays in NumPy. |\n| [Structured-Data-NumPy](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.09-Structured-Data-NumPy.ipynb) | Learn about structured data: NumPy's structured arrays. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_50e2d1f52270.png\">\n\u003C\u002Fp>\n\n## python-data\n\nIPython Notebook(s) demonstrating Python functionality geared towards data analysis.\n\n| Notebook | Description |\n|-----------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|\n| [data structures](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fstructs.ipynb) | Learn Python basics with tuples, lists, dicts, sets. |\n| [data structure utilities](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fstructs_utils.ipynb) | Learn Python operations such as slice, range, xrange, bisect, sort, sorted, reversed, enumerate, zip, list comprehensions. |\n| [functions](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Ffunctions.ipynb) | Learn about more advanced Python features: Functions as objects, lambda functions, closures, *args, **kwargs currying, generators, generator expressions, itertools. |\n| [datetime](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fdatetime.ipynb) | Learn how to work with Python dates and times: datetime, strftime, strptime, timedelta. |\n| [logging](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Flogs.ipynb) | Learn about Python logging with RotatingFileHandler and TimedRotatingFileHandler. |\n| [pdb](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fpdb.ipynb) | Learn how to debug in Python with the interactive source code debugger. |\n| [unit tests](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Funit_tests.ipynb) | Learn how to test in Python with Nose unit tests. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_a5a8d34c2278.png\">\n\u003C\u002Fp>\n\n## kaggle-and-business-analyses\n\nIPython Notebook(s) used in [kaggle](https:\u002F\u002Fwww.kaggle.com\u002F) competitions and business analyses.\n\n| Notebook | Description |\n|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|\n| [titanic](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fkaggle\u002Ftitanic.ipynb) | Predict survival on the Titanic.  Learn data cleaning, exploratory data analysis, and machine learning. |\n| [churn-analysis](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fanalyses\u002Fchurn.ipynb) | Predict customer churn.  Exercise logistic regression, gradient boosting classifers, support vector machines, random forests, and k-nearest-neighbors.  Includes discussions of confusion matrices, ROC plots, feature importances, prediction probabilities, and calibration\u002Fdescrimination.|\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_9be6a5e17d4e.png\">\n\u003C\u002Fp>\n\n## spark\n\nIPython Notebook(s) demonstrating spark and HDFS functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|\n| [spark](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fspark\u002Fspark.ipynb) | In-memory cluster computing framework, up to 100 times faster for certain applications and is well suited for machine learning algorithms. |\n| [hdfs](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fspark\u002Fhdfs.ipynb) | Reliably stores very large files across machines in a large cluster. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_51a67b8f1e2c.png\">\n\u003C\u002Fp>\n\n## mapreduce-python\n\nIPython Notebook(s) demonstrating Hadoop MapReduce with mrjob functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|\n| [mapreduce-python](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmapreduce\u002Fmapreduce-python.ipynb) | Runs MapReduce jobs in Python, executing jobs locally or on Hadoop clusters. Demonstrates Hadoop Streaming in Python code with unit test and [mrjob](https:\u002F\u002Fgithub.com\u002FYelp\u002Fmrjob) config file to analyze Amazon S3 bucket logs on Elastic MapReduce.  [Disco](https:\u002F\u002Fgithub.com\u002Fdiscoproject\u002Fdisco\u002F) is another python-based alternative.|\n\n\u003Cbr\u002F>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_75eb0ecb9a37.png\">\n\u003C\u002Fp>\n\n## aws\n\nIPython Notebook(s) demonstrating Amazon Web Services (AWS) and AWS tools functionality.\n\n\nAlso check out:\n\n* [SAWS](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fsaws): A Supercharged AWS command line interface (CLI).\n* [Awesome AWS](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fawesome-aws): A curated list of libraries, open source repos, guides, blogs, and other resources.\n\n| Notebook | Description |\n|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [boto](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#Boto) | Official AWS SDK for Python. |\n| [s3cmd](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#s3cmd) | Interacts with S3 through the command line. |\n| [s3distcp](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#s3distcp) | Combines smaller files and aggregates them together by taking in a pattern and target file.  S3DistCp can also be used to transfer large volumes of data from S3 to your Hadoop cluster. |\n| [s3-parallel-put](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#s3-parallel-put) | Uploads multiple files to S3 in parallel. |\n| [redshift](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#redshift) | Acts as a fast data warehouse built on top of technology from massive parallel processing (MPP). |\n| [kinesis](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#kinesis) | Streams data in real time with the ability to process thousands of data streams per second. |\n| [lambda](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#lambda) | Runs code in response to events, automatically managing compute resources. |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_ab202643a12e.png\">\n\u003C\u002Fp>\n\n## commands\n\nIPython Notebook(s) demonstrating various command lines for Linux, Git, etc.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [linux](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Flinux.ipynb) | Unix-like and mostly POSIX-compliant computer operating system.  Disk usage, splitting files, grep, sed, curl, viewing running processes, terminal syntax highlighting, and Vim.|\n| [anaconda](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#anaconda) | Distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment. |\n| [ipython notebook](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#ipython-notebook) | Web-based interactive computational environment where you can combine code execution, text, mathematics, plots and rich media into a single document. |\n| [git](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#git) | Distributed revision control system with an emphasis on speed, data integrity, and support for distributed, non-linear workflows. |\n| [ruby](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#ruby) | Used to interact with the AWS command line and for Jekyll, a blog framework that can be hosted on GitHub Pages. |\n| [jekyll](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#jekyll) | Simple, blog-aware, static site generator for personal, project, or organization sites.  Renders Markdown or Textile and Liquid templates, and produces a complete, static website ready to be served by Apache HTTP Server, Nginx or another web server. |\n| [pelican](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#pelican) | Python-based alternative to Jekyll. |\n| [django](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#django) | High-level Python Web framework that encourages rapid development and clean, pragmatic design. It can be useful to share reports\u002Fanalyses and for blogging. Lighter-weight alternatives include [Pyramid](https:\u002F\u002Fgithub.com\u002FPylons\u002Fpyramid), [Flask](https:\u002F\u002Fgithub.com\u002Fpallets\u002Fflask), [Tornado](https:\u002F\u002Fgithub.com\u002Ftornadoweb\u002Ftornado), and [Bottle](https:\u002F\u002Fgithub.com\u002Fbottlepy\u002Fbottle).\n\n## misc\n\nIPython Notebook(s) demonstrating miscellaneous functionality.\n\n| Notebook | Description |\n|--------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [regex](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmisc\u002Fregex.ipynb) | Regular expression cheat sheet useful in data wrangling.|\n[algorithmia](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmisc\u002FAlgorithmia.ipynb) | Algorithmia is a marketplace for algorithms. This notebook showcases 4 different algorithms: Face Detection, Content Summarizer, Latent Dirichlet Allocation and Optical Character Recognition.|\n\n## notebook-installation\n\n### anaconda\n\nAnaconda is a free distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing that aims to simplify package management and deployment.\n\nFollow instructions to install [Anaconda](https:\u002F\u002Fdocs.continuum.io\u002Fanaconda\u002Finstall) or the more lightweight [miniconda](http:\u002F\u002Fconda.pydata.org\u002Fminiconda.html).\n\n### dev-setup\n\nFor detailed instructions, scripts, and tools to set up your development environment for data analysis, check out the [dev-setup](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdev-setup) repo.\n\n### running-notebooks\n\nTo view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the notebook.  More information on IPython Notebooks can be found [here.](http:\u002F\u002Fipython.org\u002Fnotebook.html)\n\n    $ git clone https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks.git\n    $ cd data-science-ipython-notebooks\n    $ jupyter notebook\n\nNotebooks tested with Python 2.7.x.\n\n## credits\n\n* [Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](http:\u002F\u002Fwww.amazon.com\u002FPython-Data-Analysis-Wrangling-IPython\u002Fdp\u002F1449319793) by Wes McKinney\n* [PyCon 2015 Scikit-learn Tutorial](https:\u002F\u002Fgithub.com\u002Fjakevdp\u002Fsklearn_pycon2015) by Jake VanderPlas\n* [Python Data Science Handbook](https:\u002F\u002Fgithub.com\u002Fjakevdp\u002FPythonDataScienceHandbook) by Jake VanderPlas\n* [Parallel Machine Learning with scikit-learn and IPython](https:\u002F\u002Fgithub.com\u002Fogrisel\u002Fparallel_ml_tutorial) by Olivier Grisel\n* [Statistical Interference Using Computational Methods in Python](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FCompStats) by Allen Downey\n* [TensorFlow Examples](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples) by Aymeric Damien\n* [TensorFlow Tutorials](https:\u002F\u002Fgithub.com\u002Fpkmital\u002Ftensorflow_tutorials) by Parag K Mital\n* [TensorFlow Tutorials](https:\u002F\u002Fgithub.com\u002Fnlintz\u002FTensorFlow-Tutorials) by Nathan Lintz\n* [TensorFlow Tutorials](https:\u002F\u002Fgithub.com\u002Falrojo\u002Ftensorflow-tutorial) by Alexander R Johansen\n* [TensorFlow Book](https:\u002F\u002Fgithub.com\u002FBinRoot\u002FTensorFlow-Book) by Nishant Shukla\n* [Summer School 2015](https:\u002F\u002Fgithub.com\u002Fmila-udem\u002Fsummerschool2015) by mila-udem\n* [Keras tutorials](https:\u002F\u002Fgithub.com\u002Fleriomaggio\u002Fdeep-learning-keras-tensorflow) by Valerio Maggio\n* [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002F)\n* [Yhat Blog](http:\u002F\u002Fblog.yhat.com\u002F)\n\n## contributing\n\nContributions are welcome!  For bug reports or requests please [submit an issue](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues).\n\n## contact-info\n\nFeel free to contact me to discuss any issues, questions, or comments.\n\n* Email: [donne.martin@gmail.com](mailto:donne.martin@gmail.com)\n* Twitter: [@donne_martin](https:\u002F\u002Ftwitter.com\u002Fdonne_martin)\n* GitHub: [donnemartin](https:\u002F\u002Fgithub.com\u002Fdonnemartin)\n* LinkedIn: [donnemartin](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fdonnemartin)\n* Website: [donnemartin.com](http:\u002F\u002Fdonnemartin.com)\n\n## license\n\nThis repository contains a variety of content; some developed by Donne Martin, and some from third-parties.  The third-party content is distributed under the license provided by those parties.\n\nThe content developed by Donne Martin is distributed under the following license:\n\n*I am providing code and resources in this repository to you under an open source license.  Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).*\n\n    Copyright 2015 Donne Martin\n\n    Licensed under the Apache License, Version 2.0 (the \"License\");\n    you may not use this file except in compliance with the License.\n    You may obtain a copy of the License at\n\n       http:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0\n\n    Unless required by applicable law or agreed to in writing, software\n    distributed under the License is distributed on an \"AS IS\" BASIS,\n    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n    See the License for the specific language governing permissions and\n    limitations under the License.\n","\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_95f950761158.gif\">\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_8f54cd3c93b2.png\">\n  \u003Cbr\u002F>\n\u003C\u002Fp>\n\n# 数据科学 IPython 笔记本\n\n## 索引\n\n* [深度学习](#deep-learning)\n    * [TensorFlow](#tensor-flow-tutorials)\n    * [Theano](#theano-tutorials)\n    * [Keras](#keras-tutorials)\n    * [Caffe](#deep-learning-misc)\n* [scikit-learn](#scikit-learn)\n* [统计推断 - SciPy](#statistical-inference-scipy)\n* [Pandas](#pandas)\n* [Matplotlib](#matplotlib)\n* [NumPy](#numpy)\n* [Python 数据处理](#python-data)\n* [Kaggle 与商业分析](#kaggle-and-business-analyses)\n* [Spark](#spark)\n* [MapReduce - Python](#mapreduce-python)\n* [亚马逊云服务](#aws)\n* [命令行](#commands)\n* [杂项](#misc)\n* [笔记本安装](#notebook-installation)\n* [致谢](#credits)\n* [贡献](#contributing)\n* [联系方式](#contact-info)\n* [许可证](#license)\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"http:\u002F\u002Fi.imgur.com\u002FZhKXrKZ.png\">\n\u003C\u002Fp>\n\n## 深度学习\n\n演示深度学习功能的 IPython 笔记本。\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_fef6d0bb0d1b.png\">\n\u003C\u002Fp>\n\n### TensorFlow 教程\n\n额外的 TensorFlow 教程：\n\n* [pkmital\u002Ftensorflow_tutorials](https:\u002F\u002Fgithub.com\u002Fpkmital\u002Ftensorflow_tutorials)\n* [nlintz\u002FTensorFlow-Tutorials](https:\u002F\u002Fgithub.com\u002Fnlintz\u002FTensorFlow-Tutorials)\n* [alrojo\u002Ftensorflow-tutorial](https:\u002F\u002Fgithub.com\u002Falrojo\u002Ftensorflow-tutorial)\n* [BinRoot\u002FTensorFlow-Book](https:\u002F\u002Fgithub.com\u002FBinRoot\u002FTensorFlow-Book)\n* [tuanavu\u002Ftensorflow-basic-tutorials](https:\u002F\u002Fgithub.com\u002Ftuanavu\u002Ftensorflow-basic-tutorials)\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tsf-basics](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F1_intro\u002Fbasic_operations.ipynb) | 学习 TensorFlow 中的基本操作，TensorFlow 是 Google 开发的一个用于各种感知和语言理解任务的库。 |\n| [tsf-linear](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F2_basic_classifiers\u002Flinear_regression.ipynb) | 在 TensorFlow 中实现线性回归。 |\n| [tsf-logistic](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F2_basic_classifiers\u002Flogistic_regression.ipynb) | 在 TensorFlow 中实现逻辑回归。 |\n| [tsf-nn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F2_basic_classifiers\u002Fnearest_neighbor.ipynb) | 在 TensorFlow 中实现最近邻算法。 |\n| [tsf-alex](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Falexnet.ipynb) | 在 TensorFlow 中实现 AlexNet。 |\n| [tsf-cnn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Fconvolutional_network.ipynb) | 在 TensorFlow 中实现卷积神经网络。 |\n| [tsf-mlp](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Fmultilayer_perceptron.ipynb) | 在 TensorFlow 中实现多层感知器。 |\n| [tsf-rnn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F3_neural_networks\u002Frecurrent_network.ipynb) | 在 TensorFlow 中实现循环神经网络。 |\n| [tsf-gpu](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F4_multi_gpu\u002Fmultigpu_basics.ipynb) | 学习 TensorFlow 中的基本多 GPU 计算。 |\n| [tsf-gviz](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F5_ui\u002Fgraph_visualization.ipynb) | 学习 TensorFlow 中的图可视化。 |\n| [tsf-lviz](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F5_ui\u002Floss_visualization.ipynb) | 学习 TensorFlow 中的损失可视化。\n\n### TensorFlow 练习\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tsf-not-mnist](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F1_notmnist.ipynb) | 学习简单的数据整理，通过创建包含训练、开发和测试数据集的 pickle 文件来为 TensorFlow 准备数据。 |\n| [tsf-fully-connected](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F2_fullyconnected.ipynb) | 使用逻辑回归和神经网络逐步训练更深层、更精确的模型。 |\n| [tsf-regularization](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F3_regularization.ipynb) | 通过训练全连接网络对 notMNIST 字符进行分类，探索正则化技术。 |\n| [tsf-convolutions](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F4_convolutions.ipynb) | 在 TensorFlow 中创建卷积神经网络。 |\n| [tsf-word2vec](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F5_word2vec.ipynb) | 在 TensorFlow 中基于 Text8 数据训练 skip-gram 模型。 |\n| [tsf-lstm](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftensor-flow-exercises\u002F6_lstm.ipynb) | 在 TensorFlow 中基于 Text8 数据训练 LSTM 字符模型。\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"http:\u002F\u002Fwww.deeplearning.net\u002Fsoftware\u002Ftheano\u002F_static\u002Ftheano_logo_allblue_200x46.png\">\n\u003C\u002Fp>\n\n### theano教程\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [theano-intro](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Fintro_theano\u002Fintro_theano.ipynb) | Theano简介，它允许你高效地定义、优化和评估涉及多维数组的数学表达式。它可以利用GPU，并进行高效的符号微分。 |\n| [theano-scan](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Fscan_tutorial\u002Fscan_tutorial.ipynb) | 学习扫描操作，这是在Theano图中执行循环的一种机制。 |\n| [theano-logistic](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Fintro_theano\u002Flogistic_regression.ipynb) | 在Theano中实现逻辑回归。 |\n| [theano-rnn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Frnn_tutorial\u002Fsimple_rnn.ipynb) | 在Theano中实现循环神经网络。 |\n| [theano-mlp](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Ftheano-tutorial\u002Ftheano_mlp\u002Ftheano_mlp.ipynb) | 在Theano中实现多层感知机。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"http:\u002F\u002Fi.imgur.com\u002FL45Q8c2.jpg\">\n\u003C\u002Fp>\n\n### keras教程\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| keras | Keras是一个用Python编写的开源神经网络库。它可以运行在TensorFlow或Theano之上。 |\n| [setup](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F0.%20Preamble.ipynb) | 了解本教程的目标以及如何设置你的Keras环境。 |\n| [intro-deep-learning-ann](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.1%20Introduction%20-%20Deep%20Learning%20and%20ANN.ipynb) | 通过Keras和人工神经网络（ANN）入门深度学习。 |\n| [theano](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.2%20Introduction%20-%20Theano.ipynb) | 通过处理权重矩阵和梯度来学习Theano。 |\n| [keras-otto](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.3%20Introduction%20-%20Keras.ipynb) | 通过Kaggle Otto挑战来学习Keras。 |\n| [ann-mnist](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F1.4%20%28Extra%29%20A%20Simple%20Implementation%20of%20ANN%20for%20MNIST.ipynb) | 回顾使用Keras对MNIST数据集的简单ANN实现。 |\n| [conv-nets](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.1%20Supervised%20Learning%20-%20ConvNets.ipynb) | 使用Keras学习卷积神经网络（CNNs）。 |\n| [conv-net-1](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.2.1%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20I.ipynb) | 使用Keras识别MNIST中的手写数字——第一部分。 |\n| [conv-net-2](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.2.2%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20II.ipynb) | 使用Keras识别MNIST中的手写数字——第二部分。 |\n| [keras-models](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F2.3%20Supervised%20Learning%20-%20Famous%20Models%20with%20Keras.ipynb) | 使用Keras调用预训练模型，如VGG16、VGG19、ResNet50和Inception v3。 |\n| [auto-encoders](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F3.1%20Unsupervised%20Learning%20-%20AutoEncoders%20and%20Embeddings.ipynb) | 使用Keras学习自动编码器。 |\n| [rnn-lstm](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F3.2%20RNN%20and%20LSTM.ipynb) | 使用Keras学习循环神经网络（RNNs）。 |\n| [lstm-sentence-gen](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fkeras-tutorial\u002F3.3%20%28Extra%29%20LSTM%20for%20Sentence%20Generation.ipynb) | 使用Keras的长短期记忆（LSTM）网络学习RNNs。 |\n\n### 深度学习杂项\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [deep-dream](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fdeep-learning\u002Fdeep-dream\u002Fdream.ipynb) | 基于Caffe的计算机视觉程序，利用卷积神经网络在图像中寻找并增强模式。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_1f65a7d5139b.png\">\n\u003C\u002Fp>\n\n## scikit-learn\n\n演示 scikit-learn 功能的 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [intro](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-intro.ipynb) | scikit-learn 入门笔记本。scikit-learn 为 Python 增加了对大型多维数组和矩阵的支持，并提供了一个庞大的高级数学函数库，用于对这些数组进行操作。 |\n| [knn](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-intro.ipynb#K-Nearest-Neighbors-Classifier) | 在 scikit-learn 中实现 k 近邻分类器。 |\n| [linear-reg](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-linear-reg.ipynb) | 在 scikit-learn 中实现线性回归。 |\n| [svm](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-svm.ipynb) | 在 scikit-learn 中实现带核与不带核的支持向量机分类器。 |\n| [random-forest](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-random-forest.ipynb) | 在 scikit-learn 中实现随机森林分类器和回归器。 |\n| [k-means](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-k-means.ipynb) | 在 scikit-learn 中实现 k 均值聚类。 |\n| [pca](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-pca.ipynb) | 在 scikit-learn 中实现主成分分析。 |\n| [gmm](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-gmm.ipynb) | 在 scikit-learn 中实现高斯混合模型。 |\n| [validation](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscikit-learn\u002Fscikit-learn-validation.ipynb) | 在 scikit-learn 中实现验证和模型选择。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_41fa0e3dc800.png\">\n\u003C\u002Fp>\n\n## statistical-inference-scipy\n\n演示使用 SciPy 功能进行统计推断的 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| scipy | SciPy 是基于 Python 的 NumPy 扩展构建的一组数学算法和便捷函数集合。它通过为用户提供用于数据操作和可视化的高级命令和类，显著增强了交互式 Python 会话的功能。 |\n| [effect-size](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscipy\u002Feffect_size.ipynb) | 通过分析男性和女性身高的差异，探讨量化效应大小的统计指标。利用行为风险因素监测系统 (BRFSS) 的数据，估算美国成年女性和男性的平均身高及标准差。 |\n| [sampling](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscipy\u002Fsampling.ipynb) | 通过使用 BRFSS 数据分析美国男性和女性的平均体重，探索随机抽样方法。 |\n| [hypothesis](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fscipy\u002Fhypothesis.ipynb) | 通过比较第一胎婴儿与其他婴儿的差异，探讨假设检验的方法。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_7ecca73c3570.png\">\n\u003C\u002Fp>\n\n## pandas\n\n演示 pandas 功能的 IPython Notebook。\n\n| Notebook | 描述 |\n|--------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [pandas](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002Fpandas.ipynb) | 一个用 Python 编写的用于数据处理和分析的软件库。提供用于操作数值表格和时间序列的数据结构及操作。 |\n| [github-data-wrangling](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fviz\u002Fblob\u002Fmaster\u002Fgithubstats\u002Fdata_wrangling.ipynb) | 通过分析 [`Viz`](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fviz) 仓库中的 GitHub 数据，学习如何加载、清洗、合并以及进行特征工程。 |\n| [Introduction-to-Pandas](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.00-Introduction-to-Pandas.ipynb) | Pandas 入门。 |\n| [Introducing-Pandas-Objects](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.01-Introducing-Pandas-Objects.ipynb) | 学习 Pandas 的对象。 |\n| [Data Indexing and Selection](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.02-Data-Indexing-and-Selection.ipynb) | 学习 Pandas 中的数据索引与选择。 |\n| [Operations-in-Pandas](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.03-Operations-in-Pandas.ipynb) | 学习在 Pandas 中对数据进行操作。 |\n| [Missing-Values](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.04-Missing-Values.ipynb) | 学习如何处理 Pandas 中的缺失数据。 |\n| [Hierarchical-Indexing](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.05-Hierarchical-Indexing.ipynb) | 学习 Pandas 中的层次化索引。 |\n| [Concat-And-Append](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.06-Concat-And-Append.ipynb) | 学习如何在 Pandas 中组合数据集：使用 concat 和 append。 |\n| [Merge-and-Join](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.07-Merge-and-Join.ipynb) | 学习如何在 Pandas 中组合数据集：使用 merge 和 join。 |\n| [Aggregation-and-Grouping](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.08-Aggregation-and-Grouping.ipynb) | 学习 Pandas 中的聚合与分组操作。 |\n| [Pivot-Tables](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.09-Pivot-Tables.ipynb) | 学习 Pandas 中的透视表。 |\n| [Working-With-Strings](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.10-Working-With-Strings.ipynb) | 学习 Pandas 中的向量化字符串操作。 |\n| [Working-with-Time-Series](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.11-Working-with-Time-Series.ipynb) | 学习如何在 Pandas 中处理时间序列数据。 |\n| [Performance-Eval-and-Query](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpandas\u002F03.12-Performance-Eval-and-Query.ipynb) | 学习高性能 Pandas：eval() 和 query() 方法。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_1f733496b99a.png\">\n\u003C\u002Fp>\n\n## matplotlib\n\n展示 matplotlib 功能的 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|-----------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [matplotlib](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002Fmatplotlib.ipynb) | Python 2D 绘图库，可在多种打印格式和跨平台的交互式环境中生成出版质量的图表。 |\n| [matplotlib-applied](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002Fmatplotlib-applied.ipynb) | 将 matplotlib 可视化应用于 Kaggle 竞赛中的探索性数据分析。学习如何创建条形图、直方图、subplot2grid 布局、归一化图表、散点图、子图以及核密度估计图。 |\n| [Introduction-To-Matplotlib](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.00-Introduction-To-Matplotlib.ipynb) | matplotlib 入门。 |\n| [Simple-Line-Plots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.01-Simple-Line-Plots.ipynb) | 学习 matplotlib 中的简单折线图。 |\n| [Simple-Scatter-Plots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.02-Simple-Scatter-Plots.ipynb) | 学习 matplotlib 中的简单散点图。 |\n| [Errorbars.ipynb](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.03-Errorbars.ipynb) | 学习在 matplotlib 中可视化误差。 |\n| [Density-and-Contour-Plots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.04-Density-and-Contour-Plots.ipynb) | 学习 matplotlib 中的密度图和等高线图。 |\n| [Histograms-and-Binnings](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.05-Histograms-and-Binnings.ipynb) | 学习 matplotlib 中的直方图、分箱和密度估计。 |\n| [Customizing-Legends](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.06-Customizing-Legends.ipynb) | 学习在 matplotlib 中自定义图例。 |\n| [Customizing-Colorbars](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.07-Customizing-Colorbars.ipynb) | 学习在 matplotlib 中自定义颜色条。 |\n| [Multiple-Subplots](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.08-Multiple-Subplots.ipynb) | 学习 matplotlib 中的多个子图。 |\n| [Text-and-Annotation](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.09-Text-and-Annotation.ipynb) | 学习 matplotlib 中的文本和注释。 |\n| [Customizing-Ticks](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.10-Customizing-Ticks.ipynb) | 学习在 matplotlib 中自定义刻度。 |\n| [Settings-and-Stylesheets](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.11-Settings-and-Stylesheets.ipynb) | 学习自定义 matplotlib：配置和样式表。 |\n| [Three-Dimensional-Plotting](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.12-Three-Dimensional-Plotting.ipynb) | 学习 matplotlib 中的三维绘图。 |\n| [Geographic-Data-With-Basemap](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.13-Geographic-Data-With-Basemap.ipynb) | 学习在 matplotlib 中使用 basemap 处理地理数据。 |\n| [Visualization-With-Seaborn](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmatplotlib\u002F04.14-Visualization-With-Seaborn.ipynb) | 学习使用 Seaborn 进行可视化。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_d8b18f24aab6.png\">\n\u003C\u002Fp>\n\n## numpy\n\n演示 NumPy 功能的 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [numpy](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002Fnumpy.ipynb) | 为 Python 添加对大型多维数组和矩阵的支持，并提供大量用于操作这些数组的高级数学函数库。 |\n| [Introduction-to-NumPy](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.00-Introduction-to-NumPy.ipynb) | NumPy 简介。 |\n| [Understanding-Data-Types](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.01-Understanding-Data-Types.ipynb) | 学习 Python 中的数据类型。 |\n| [The-Basics-Of-NumPy-Arrays](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.02-The-Basics-Of-NumPy-Arrays.ipynb) | 学习 NumPy 数组的基础知识。 |\n| [Computation-on-arrays-ufuncs](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.03-Computation-on-arrays-ufuncs.ipynb) | 学习 NumPy 数组上的计算：通用函数。 |\n| [Computation-on-arrays-aggregates](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.04-Computation-on-arrays-aggregates.ipynb) | 学习聚合操作：NumPy 中的最小值、最大值以及介于两者之间的所有内容。 |\n| [Computation-on-arrays-broadcasting](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.05-Computation-on-arrays-broadcasting.ipynb) | 学习数组上的计算：NumPy 中的广播机制。 |\n| [Boolean-Arrays-and-Masks](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.06-Boolean-Arrays-and-Masks.ipynb) | 学习 NumPy 中的比较、掩码和布尔逻辑。 |\n| [Fancy-Indexing](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.07-Fancy-Indexing.ipynb) | 学习 NumPy 中的高级索引技术。 |\n| [Sorting](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.08-Sorting.ipynb) | 学习 NumPy 中的数组排序。 |\n| [Structured-Data-NumPy](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fnumpy\u002F02.09-Structured-Data-NumPy.ipynb) | 学习结构化数据：NumPy 的结构化数组。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_50e2d1f52270.png\">\n\u003C\u002Fp>\n\n## python-data\n\n面向数据分析的 Python 功能演示 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|-----------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------|\n| [data structures](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fstructs.ipynb) | 使用元组、列表、字典、集合学习 Python 基础知识。 |\n| [data structure utilities](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fstructs_utils.ipynb) | 学习 Python 操作，如切片、range、xrange、bisect、sort、sorted、reversed、enumerate、zip 以及列表推导式。 |\n| [functions](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Ffunctions.ipynb) | 学习更高级的 Python 特性：函数作为对象、lambda 函数、闭包、*args、**kwargs currying、生成器、生成器表达式、itertools。 |\n| [datetime](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fdatetime.ipynb) | 学习如何使用 Python 处理日期和时间：datetime、strftime、strptime、timedelta。 |\n| [logging](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Flogs.ipynb) | 学习使用 RotatingFileHandler 和 TimedRotatingFileHandler 进行 Python 日志记录。 |\n| [pdb](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Fpdb.ipynb) | 学习使用交互式源代码调试器在 Python 中进行调试。 |\n| [unit tests](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fpython-data\u002Funit_tests.ipynb) | 学习使用 Nose 单元测试框架进行 Python 测试。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_a5a8d34c2278.png\">\n\u003C\u002Fp>\n\n## kaggle-and-business-analyses\n\n用于 [kaggle](https:\u002F\u002Fwww.kaggle.com\u002F) 竞赛和商业分析的 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|-------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|\n| [titanic](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fkaggle\u002Ftitanic.ipynb) | 预测泰坦尼克号乘客的生存情况。学习数据清洗、探索性数据分析和机器学习。 |\n| [churn-analysis](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fanalyses\u002Fchurn.ipynb) | 预测客户流失。实践逻辑回归、梯度提升分类器、支持向量机、随机森林和 k 最近邻算法。包含混淆矩阵、ROC 曲线、特征重要性、预测概率以及校准\u002F区分度的讨论。|\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_9be6a5e17d4e.png\">\n\u003C\u002Fp>\n\n## spark\n\n展示 Spark 和 HDFS 功能的 IPython Notebook。\n\n| Notebook | 描述 |\n|--------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|\n| [spark](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fspark\u002Fspark.ipynb) | 基于内存的集群计算框架，在某些应用场景下速度可提升至 100 倍，非常适合机器学习算法。 |\n| [hdfs](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fspark\u002Fhdfs.ipynb) | 可靠地将超大文件跨多台机器存储在一个大型集群中。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_51a67b8f1e2c.png\">\n\u003C\u002Fp>\n\n## mapreduce-python\n\n展示使用 mrjob 实现 Hadoop MapReduce 功能的 IPython Notebook。\n\n| Notebook | 描述 |\n|--------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|\n| [mapreduce-python](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmapreduce\u002Fmapreduce-python.ipynb) | 以 Python 运行 MapReduce 作业，可在本地或 Hadoop 集群上执行。演示了如何在 Python 代码中使用 Hadoop Streaming，并结合单元测试及 [mrjob](https:\u002F\u002Fgithub.com\u002FYelp\u002Fmrjob) 配置文件来分析 Elastic MapReduce 上的 Amazon S3 存储桶日志。[Disco](https:\u002F\u002Fgithub.com\u002Fdiscoproject\u002Fdisco\u002F) 是另一个基于 Python 的替代方案。|\n\n\u003Cbr\u002F>\n\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_75eb0ecb9a37.png\">\n\u003C\u002Fp>\n\n## aws\n\n展示亚马逊云服务（AWS）及其工具功能的 IPython Notebook。\n\n还可查看：\n\n* [SAWS](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fsaws)：一款功能强大的 AWS 命令行界面（CLI）。\n* [Awesome AWS](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fawesome-aws)：精选的库、开源项目、指南、博客及其他资源列表。\n\n| Notebook | 描述 |\n|------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [boto](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#Boto) | 官方的 AWS Python SDK。 |\n| [s3cmd](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#s3cmd) | 通过命令行与 S3 进行交互。 |\n| [s3distcp](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#s3distcp) | 根据指定模式和目标文件，将较小的文件合并并聚合在一起。S3DistCp 还可用于将大量数据从 S3 传输到您的 Hadoop 集群。 |\n| [s3-parallel-put](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#s3-parallel-put) | 并行上传多个文件至 S3。 |\n| [redshift](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#redshift) | 作为基于大规模并行处理（MPP）技术构建的快速数据仓库。 |\n| [kinesis](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#kinesis) | 实时流式传输数据，每秒可处理数千个数据流。 |\n| [lambda](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Faws\u002Faws.ipynb#lambda) | 根据事件触发运行代码，并自动管理计算资源。 |\n\n\u003Cbr\u002F>\n\u003Cp align=\"center\">\n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_readme_ab202643a12e.png\">\n\u003C\u002Fp>\n\n## 命令\n\n展示 Linux、Git 等各种命令行的 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [linux](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Flinux.ipynb) | 类 Unix 且大多符合 POSIX 标准的计算机操作系统。包括磁盘使用情况、文件分割、grep、sed、curl、查看运行中的进程、终端语法高亮以及 Vim。|\n| [anaconda](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#anaconda) | 面向大规模数据处理、预测分析和科学计算的 Python 编程语言发行版，旨在简化包管理和部署。|\n| [ipython notebook](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#ipython-notebook) | 基于 Web 的交互式计算环境，可将代码执行、文本、数学公式、图表和富媒体整合到一个文档中。|\n| [git](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#git) | 分布式版本控制系统，强调速度、数据完整性以及对分布式、非线性工作流的支持。|\n| [ruby](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#ruby) | 用于与 AWS 命令行交互，以及 Jekyll——一个可在 GitHub Pages 上托管的博客框架。|\n| [jekyll](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#jekyll) | 简单、具备博客功能的静态网站生成器，适用于个人、项目或组织的网站。它会渲染 Markdown 或 Textile 和 Liquid 模板，并生成一个完整的静态网站，可由 Apache HTTP Server、Nginx 或其他 Web 服务器提供服务。|\n| [pelican](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#pelican) | 基于 Python 的 Jekyll 替代方案。|\n| [django](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fcommands\u002Fmisc.ipynb#django) | 高级 Python Web 框架，鼓励快速开发及简洁、实用的设计。可用于分享报告\u002F分析内容，也可用于博客。更轻量级的替代方案包括 [Pyramid](https:\u002F\u002Fgithub.com\u002FPylons\u002Fpyramid)、[Flask](https:\u002F\u002Fgithub.com\u002Fpallets\u002Fflask)、[Tornado](https:\u002F\u002Fgithub.com\u002Ftornadoweb\u002Ftornado) 和 [Bottle](https:\u002F\u002Fgithub.com\u002Fbottlepy\u002Fbottle)。\n\n## 杂项\n\n展示各种功能的 IPython 笔记本。\n\n| 笔记本 | 描述 |\n|--------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [regex](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmisc\u002Fregex.ipynb) | 正则表达式速查表，适用于数据清洗。|\n|[algorithmia](http:\u002F\u002Fnbviewer.ipython.org\u002Fgithub\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fblob\u002Fmaster\u002Fmisc\u002FAlgorithmia.ipynb) | Algorithmia 是一个算法市场。该笔记本展示了四种不同的算法：人脸检测、内容摘要、潜在狄利克雷分配和光学字符识别。|\n\n## 笔记本安装\n\n### anaconda\n\nAnaconda 是面向大规模数据处理、预测分析和科学计算的免费 Python 发行版，旨在简化包管理和部署。\n\n请按照说明安装 [Anaconda](https:\u002F\u002Fdocs.continuum.io\u002Fanaconda\u002Finstall) 或更轻量级的 [miniconda](http:\u002F\u002Fconda.pydata.org\u002Fminiconda.html)。\n\n### 开发环境搭建\n\n如需详细说明、脚本和工具来设置您的数据分析开发环境，请查看 [dev-setup](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdev-setup) 仓库。\n\n### 运行笔记本\n\n要查看交互式内容或修改 IPython 笔记本中的元素，您需要先克隆或下载仓库，然后运行笔记本。有关 IPython 笔记本的更多信息，请参阅 [此处](http:\u002F\u002Fipython.org\u002Fnotebook.html)。\n\n    $ git clone https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks.git\n    $ cd data-science-ipython-notebooks\n    $ jupyter notebook\n\n笔记本已在 Python 2.7.x 上测试通过。\n\n## 致谢\n\n* 《Python 数据分析：使用 Pandas、NumPy 和 IPython 进行数据清洗》（作者：Wes McKinney）[链接](http:\u002F\u002Fwww.amazon.com\u002FPython-Data-Analysis-Wrangling-IPython\u002Fdp\u002F1449319793)\n* 2015 年 PyCon scikit-learn 教程（作者：Jake VanderPlas）[链接](https:\u002F\u002Fgithub.com\u002Fjakevdp\u002Fsklearn_pycon2015)\n* 《Python 数据科学手册》（作者：Jake VanderPlas）[链接](https:\u002F\u002Fgithub.com\u002Fjakevdp\u002FPythonDataScienceHandbook)\n* 使用 scikit-learn 和 IPython 进行并行机器学习教程（作者：Olivier Grisel）[链接](https:\u002F\u002Fgithub.com\u002Fogrisel\u002Fparallel_ml_tutorial)\n* 使用 Python 计算方法进行统计推断（作者：Allen Downey）[链接](https:\u002F\u002Fgithub.com\u002FAllenDowney\u002FCompStats)\n* TensorFlow 示例（作者：Aymeric Damien）[链接](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples)\n* TensorFlow 教程（作者：Parag K Mital）[链接](https:\u002F\u002Fgithub.com\u002Fpkmital\u002Ftensorflow_tutorials)\n* TensorFlow 教程（作者：Nathan Lintz）[链接](https:\u002F\u002Fgithub.com\u002Fnlintz\u002FTensorFlow-Tutorials)\n* TensorFlow 教程（作者：Alexander R Johansen）[链接](https:\u002F\u002Fgithub.com\u002Falrojo\u002Ftensorflow-tutorial)\n* TensorFlow 书籍（作者：Nishant Shukla）[链接](https:\u002F\u002Fgithub.com\u002FBinRoot\u002FTensorFlow-Book)\n* 2015 年夏季学校（由 mila-udem 组织）[链接](https:\u002F\u002Fgithub.com\u002Fmila-udem\u002Fsummerschool2015)\n* Keras 教程（作者：Valerio Maggio）[链接](https:\u002F\u002Fgithub.com\u002Fleriomaggio\u002Fdeep-learning-keras-tensorflow)\n* Kaggle [网站](https:\u002F\u002Fwww.kaggle.com\u002F)\n* Yhat 博客 [网站](http:\u002F\u002Fblog.yhat.com\u002F)\n\n## 贡献\n\n欢迎贡献！如发现 bug 或有需求，请 [提交 issue](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues)。\n\n## 联系方式\n\n如有任何问题、疑问或意见，欢迎随时联系我。\n\n* 邮箱：[donne.martin@gmail.com](mailto:donne.martin@gmail.com)\n* Twitter：[@donne_martin](https:\u002F\u002Ftwitter.com\u002Fdonne_martin)\n* GitHub：[donnemartin](https:\u002F\u002Fgithub.com\u002Fdonnemartin)\n* LinkedIn：[donnemartin](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fdonnemartin)\n* 网站：[donnemartin.com](http:\u002F\u002Fdonnemartin.com)\n\n## 许可证\n\n本仓库包含多种内容，其中一部分由Donne Martin开发，另一部分来自第三方。第三方内容根据其各自提供的许可证进行分发。\n\n由Donne Martin开发的内容依据以下许可证进行分发：\n\n*我在此仓库中提供的代码和资源均采用开源许可证授权给您。由于这是我的个人仓库，您所获得的对我代码和资源的使用许可来自我本人，而非我的雇主（Facebook）。*\n\n    版权所有 © 2015 Donne Martin\n\n    根据Apache许可证第2.0版（“许可证”）授权；\n\n    除非遵守该许可证的规定，否则不得使用本文件。\n\n    您可以在以下网址获取许可证的副本：\n\n       http:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0\n\n    除非适用法律要求或双方书面同意，否则软件按“原样”提供，不提供任何形式的担保或条件。有关具体语言的权限及限制，请参阅该许可证。","# data-science-ipython-notebooks 快速上手指南\n\n`data-science-ipython-notebooks` 是一个汇集了数据科学核心领域（深度学习、机器学习、数据分析等）的 IPython Notebook 教程集合。本指南将帮助你快速搭建环境并运行这些示例代码。\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**：Linux, macOS 或 Windows (推荐 WSL2)\n*   **Python 版本**：建议 Python 3.8 及以上\n*   **前置依赖**：\n    *   `pip` 或 `conda` (推荐使用 Anaconda\u002FMiniconda 管理环境)\n    *   `git` (用于克隆仓库)\n    *   **硬件加速 (可选)**：若需运行深度学习 (TensorFlow\u002FTheano\u002FKeras) 示例，建议配备 NVIDIA GPU 并安装对应的 CUDA\u002FcuDNN。\n\n> **国内开发者提示**：为避免下载依赖包速度过慢，建议配置国内镜像源。\n> *   **pip**: `pip config set global.index-url https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple`\n> *   **conda**: `conda config --add channels https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Fpkgs\u002Fmain\u002F`\n\n## 安装步骤\n\n### 1. 克隆项目仓库\n\n使用 git 将项目下载到本地：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks.git\ncd data-science-ipython-notebooks\n```\n\n### 2. 创建虚拟环境\n\n推荐使用 conda 创建一个隔离环境，以避免依赖冲突：\n\n```bash\nconda create -n ds-notebooks python=3.9\nconda activate ds-notebooks\n```\n\n### 3. 安装核心依赖库\n\n根据目录结构，该项目涵盖了多个主流库。你可以一次性安装基础数据科学栈，或按需安装深度学习框架。\n\n**安装基础数据科学栈 (Pandas, NumPy, Scikit-learn, Matplotlib 等):**\n\n```bash\npip install pandas numpy scipy matplotlib scikit-learn jupyter seaborn\n```\n\n**安装深度学习框架 (按需选择):**\n\n*   **TensorFlow:**\n    ```bash\n    pip install tensorflow\n    # 若有 GPU，可安装 tensorflow-gpu (TF 2.x 已内置 GPU 支持，只需确保驱动正常)\n    ```\n*   **Keras:** (通常随 TensorFlow 安装，也可独立安装)\n    ```bash\n    pip install keras\n    ```\n*   **Theano:** (注：Theano 已停止维护，仅建议用于学习旧代码，需特定版本)\n    ```bash\n    pip install Theano\n    ```\n\n### 4. 启动 Jupyter Notebook\n\n在项目根目录下启动服务：\n\n```bash\njupyter notebook\n```\n\n浏览器会自动打开，即可看到各个分类的文件夹（如 `deep-learning`, `scikit-learn` 等）。\n\n## 基本使用\n\n本项目以交互式笔记本形式组织内容，无需编写额外脚本即可直接运行学习。\n\n### 1. 浏览教程目录\n在 Jupyter 界面中，进入你感兴趣的领域，例如深度学习中的 TensorFlow 教程：\n*   路径：`deep-learning\u002Ftensor-flow-examples\u002Fnotebooks\u002F1_intro\u002F`\n*   文件：`basic_operations.ipynb`\n\n### 2. 运行示例代码\n点击 `.ipynb` 文件打开笔记本。\n*   **查看代码**：每个单元格 (Cell) 包含具体的实现代码和说明。\n*   **执行代码**：选中一个代码单元格，按下 `Shift + Enter` 运行。\n*   **观察结果**：代码下方会直接显示输出结果、图表或模型训练进度。\n\n### 3. 最小化实践示例\n以 **Pandas** 数据分析为例，你可以直接新建一个 Notebook 或修改现有文件，运行以下标准代码测试环境：\n\n```python\nimport pandas as pd\nimport numpy as np\n\n# 创建简单的 DataFrame\ndata = {'Name': ['Alice', 'Bob', 'Charlie'],\n        'Age': [25, 30, 35],\n        'City': ['Beijing', 'Shanghai', 'Shenzhen']}\n\ndf = pd.DataFrame(data)\n\n# 显示前几行\nprint(df.head())\n\n# 简单统计\nprint(df['Age'].mean())\n```\n\n### 4. 探索特定主题\n项目索引涵盖了以下核心模块，可直接跳转对应文件夹学习：\n*   **深度学习**: `deep-learning\u002F` (含 TensorFlow, Keras, Theano 实战)\n*   **机器学习**: `scikit-learn\u002F` (经典算法实现)\n*   **数据处理**: `pandas\u002F`, `numpy\u002F`\n*   **可视化**: `matplotlib\u002F`\n*   **大数据**: `spark\u002F`, `mapreduce-python\u002F`\n*   **云平台**: `amazon web services\u002F` (AWS 相关操作)\n\n通过直接运行这些现成的 Notebook，你可以快速复现经典数据科学工作流，并根据注释修改参数进行实验。","某电商公司的数据科学团队正急需构建一个商品图像分类模型，以自动识别用户上传的服装风格，但团队中部分成员对深度学习框架的底层实现尚不熟悉。\n\n### 没有 data-science-ipython-notebooks 时\n- **环境配置耗时**：工程师需花费数天时间独自摸索 TensorFlow 或 Keras 的环境搭建，常因版本依赖冲突导致项目启动受阻。\n- **代码从零手写**：缺乏标准参考，团队成员需重复编写基础的网络结构（如 CNN、RNN），不仅效率低下且容易引入难以排查的 Bug。\n- **学习曲线陡峭**：新手在面对复杂的矩阵运算和梯度下降逻辑时无从下手，缺乏可视化的中间步骤演示，理解成本极高。\n- **最佳实践缺失**：由于缺少经过验证的代码模板，模型训练过程中的超参数调整和数据处理流程往往不够规范，影响最终准确率。\n\n### 使用 data-science-ipython-notebooks 后\n- **即开即用**：直接调用仓库中预置的 TensorFlow 和 Keras 教程笔记，几分钟内即可在本地复现从基础运算到 AlexNet 的完整环境。\n- **站在巨人肩膀上**：利用现成的卷积神经网络（CNN）和多層感知机（MLP）代码模板，将模型原型开发时间从数天缩短至几小时。\n- **可视化教学**：通过运行包含详细注释和中间结果展示的 Notebook，团队成员能直观理解反向传播等抽象概念，快速上手核心算法。\n- **标准化流程**：借鉴仓库中关于 Pandas 数据清洗和 Scikit-learn 评估的成熟案例，统一了团队的数据处理规范，显著提升了模型稳定性。\n\ndata-science-ipython-notebooks 通过将零散的知识点转化为可执行、可交互的标准代码库，极大地降低了深度学习项目的试错成本与入门门槛。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdonnemartin_data-science-ipython-notebooks_95f95076.gif","donnemartin","Donne Martin","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fdonnemartin_24ea3ab3.png","Tech Lead @facebook","@facebook","Washington, D.C.","donne.martin@gmail.com",null,"http:\u002F\u002Fdonnemartin.com\u002F","https:\u002F\u002Fgithub.com\u002Fdonnemartin",[84,88,92,95],{"name":85,"color":86,"percentage":87},"Python","#3572A5",100,{"name":89,"color":90,"percentage":91},"CSS","#663399",0,{"name":93,"color":94,"percentage":91},"Makefile","#427819",{"name":96,"color":97,"percentage":91},"Dockerfile","#384d54",28980,8027,"2026-04-07T04:24:53","NOASSERTION","未说明","未说明 (部分深度学习示例如 TensorFlow 多 GPU 计算可能需要 GPU，但无具体型号或版本要求)",{"notes":105,"python":102,"dependencies":106},"该项目主要是一系列数据科学和深度学习相关的 IPython 笔记本教程集合，而非单一可执行工具。它涵盖了 TensorFlow、Theano、Keras、Scikit-learn 等多个库的使用示例。由于涉及深度学习（如 CNN、RNN、LSTM）和大数据处理（Spark），实际运行特定笔记本时可能需要相应的硬件加速（GPU）和较大的内存，具体取决于所选的教程内容。建议使用虚拟环境（如 conda 或 venv）根据每个笔记本的具体需求单独安装依赖。",[107,108,109,110,111,112,113,114,115,116],"tensorflow","theano","keras","scikit-learn","scipy","pandas","matplotlib","numpy","spark","boto3 (AWS)",[14,16,118],"其他",[120,121,122,123,124,125,107,108,126,110,127,115,128,129,113,112,114,111,109],"python","machine-learning","deep-learning","data-science","big-data","aws","caffe","kaggle","mapreduce","hadoop",5,"2026-03-27T02:49:30.150509","2026-04-11T18:33:07.769907",[134,139,144,149,154,159,164],{"id":135,"question_zh":136,"answer_zh":137,"source_url":138},22682,"为什么点击笔记本链接时会出现 404 错误或链接失效？","部分 Keras 笔记本链接可能已过期或损坏。维护者已确认该问题并正在修复中。如果遇到特定链接失效，请检查仓库的最新提交记录，因为维护者通常会更新这些链接。","https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues\u002F49",{"id":140,"question_zh":141,"answer_zh":142,"source_url":143},22683,"打开笔记本链接时出现\"Error 503 No healthy backends\"错误怎么办？","这通常是由于 nbviewer 依赖的服务（如 Fastly 或 Rackspace）暂时中断导致的，而非仓库本身的问题。维护者确认链接本身是正常的，建议稍后重试。如果页面底部显示\"Delivered by Fastly, Rendered by Rackspace\"，则确认为第三方服务临时故障。","https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues\u002F33",{"id":145,"question_zh":146,"answer_zh":147,"source_url":148},22684,"如何获取项目中的代码和数据集（例如 churn 数据集）？","所有代码都直接包含在 Jupyter Notebook 文件中，可以直接在 GitHub 上查看或下载。数据集存放在仓库根目录下的 `data` 文件夹中。无需单独请求，克隆或下载整个仓库即可获取所有内容。","https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues\u002F50",{"id":150,"question_zh":151,"answer_zh":152,"source_url":153},22685,"Spark 笔记本是否支持 DataFrame 和 Dataset API？","是的，Spark 笔记本已更新以支持 DataFrame。Spark 1.3 引入了 DataFrame，1.6 引入了 Dataset API。维护者已通过提交（commit 4e8f427）更新了相关笔记本以使用 DataFrame。对于 PySpark 用户，Dataset 支持将在 Spark 2.0 中提供。","https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues\u002F21",{"id":155,"question_zh":156,"answer_zh":157,"source_url":158},22686,"项目中是否包含深度学习（Deep Learning）相关的笔记本？","是的，项目已分批次添加了深度学习笔记本。维护者已经提交了多批内容（包括 commits 5ecc149, 5281bee, e5680f2），并且明确表示如果有足够兴趣，还会添加更多如 Keras 相关的内容。","https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues\u002F17",{"id":160,"question_zh":161,"answer_zh":162,"source_url":163},22687,"是否有展示如何使用 SciPy 进行统计推断的笔记本？","有的。项目参考了 Allen Downey 在 Pycon 2015 上的研讨会内容，并已添加相关的统计推断笔记本。维护者已通过多次提交（c750b5a, 85675ab, 6a1a2af）完成了内容更新，并在致谢部分添加了原始资源链接。","https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues\u002F5",{"id":165,"question_zh":166,"answer_zh":167,"source_url":168},22688,"如何贡献新的 Keras 或其他主题的笔记本？","欢迎贡献！用户可以提出具体的笔记本建议或直接提交 Pull Request。维护者曾回应社区成员表示可以添加更多 Keras 笔记本，并询问是否有其他推荐的主题。已有贡献者通过 PR #29 等方式成功合并了内容。","https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks\u002Fissues\u002F39",[]]