[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-mbadry1--Top-Deep-Learning":3,"tool-mbadry1--Top-Deep-Learning":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},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。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},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",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},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。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":80,"owner_email":81,"owner_twitter":79,"owner_website":82,"owner_url":83,"languages":84,"stars":89,"forks":90,"last_commit_at":91,"license":92,"difficulty_score":93,"env_os":94,"env_gpu":95,"env_ram":95,"env_deps":96,"category_tags":99,"github_topics":100,"view_count":23,"oss_zip_url":79,"oss_zip_packed_at":79,"status":16,"created_at":111,"updated_at":112,"faqs":113,"releases":114},3505,"mbadry1\u002FTop-Deep-Learning","Top-Deep-Learning"," Top 200 deep learning Github repositories sorted by the number of stars.","Top-Deep-Learning 是一个精心整理的深度学习资源导航库，它基于 GitHub 星标数量，筛选并列出了全球最受欢迎的 200 个深度学习开源项目。面对海量且分散的技术资源，开发者往往难以快速辨别哪些框架或教程最具价值与活跃度，Top-Deep-Learning 通过权威的数据排序，有效解决了信息过载与筛选困难的问题，帮助用户第一时间锁定行业标杆。\n\n这份清单涵盖了从底层框架（如 TensorFlow、PyTorch、Keras）到计算机视觉库（OpenCV），再到系统的学习资料（如《深度学习 500 问》）等核心内容。其独特的技术亮点在于采用了精准的搜索策略，综合检索\"CNN\"、\"RNN\"等关键术语，并定期更新排名数据，确保列表的时效性与代表性。\n\n无论是刚入门的学生、寻求高效工具的研发工程师，还是关注前沿趋势的算法研究人员，都能从中获益。对于初学者，它是探索领域的最佳地图；对于资深从业者，它是追踪技术风向的参考坐标。Top-Deep-Learning 以简洁直观的方式，连接了人与优质的开源生态，让深度学习的学习与应用之路更加清晰顺畅。","# Top deep learning Github repositories\n\nHere's a list of top 200 deep learning Github repositories sorted by the number of stars. The query that has been used with Github search API is:\n\n- `deep-learning OR CNN OR RNN OR \"convolutional neural network\" OR \"recurrent neural network\"`\n\nTrending deep learning Github repositories can be found [here](https:\u002F\u002Fgithub.com\u002Fmbadry1\u002FTrending-Deep-Learning).\n\nDate: 02-02-2020 compared to 09-01-2019\n\nNote: This will be updated regularly.\n\n| | Pos | Name | Description | Language | Stars | Forks |\n|--------------------|-----|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|--------|-------|\n| :heavy_minus_sign: | 1 | [tensorflow](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow) | An Open Source Machine Learning Framework for Everyone | C++ | 140574 | 79704 |\n| :heavy_minus_sign: | 2 | [keras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fkeras) | Deep Learning for humans | Python | 46627 | 17671 |\n| :heavy_minus_sign: | 3 | [opencv](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fopencv) | Open Source Computer Vision Library | C++ | 41817 | 32255 |\n| :arrow_up:1 | 4 | [DeepLearning-500-questions](https:\u002F\u002Fgithub.com\u002Fscutan90\u002FDeepLearning-500-questions) | 深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06 | None | 36349 | 11201 |\n| :arrow_down:1 | 5 | [TensorFlow-Examples](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples) | TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) | Jupyter Notebook | 36173 | 13657 |\n| :heavy_minus_sign: | 6 | [pytorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch) | Tensors and Dynamic neural networks in Python with strong GPU acceleration | C++ | 35719 | 8990 |\n| :heavy_minus_sign: | 7 | [caffe](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe) | Caffe: a fast open framework for deep learning. | C++ | 29775 | 18028 |\n| :arrow_up:4 | 8 | [faceswap](https:\u002F\u002Fgithub.com\u002Fdeepfakes\u002Ffaceswap) | Deepfakes Software For All | Python | 28863 | 9258 |\n| :new: | 9 | [100-Days-Of-ML-Code](https:\u002F\u002Fgithub.com\u002FAvik-Jain\u002F100-Days-Of-ML-Code) | 100 Days of ML Coding | Python | 27766 | 6943 |\n| :arrow_down:1 | 10 | [deeplearningbook-chinese](https:\u002F\u002Fgithub.com\u002Fexacity\u002Fdeeplearningbook-chinese) | Deep Learning Book Chinese Translation | TeX | 27753 | 8098 |\n| :arrow_down:1 | 11 | [Deep-Learning-Papers-Reading-Roadmap](https:\u002F\u002Fgithub.com\u002Ffloodsung\u002FDeep-Learning-Papers-Reading-Roadmap) | Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! | Python | 25457 | 5818 |\n| :new: | 12 | [practicalAI](https:\u002F\u002Fgithub.com\u002FpracticalAI\u002FpracticalAI) | 📚 A practical approach to machine learning. | Jupyter Notebook | 23437 | 4171 |\n| :arrow_down:2 | 13 | [machine-learning-for-software-engineers](https:\u002F\u002Fgithub.com\u002FZuzooVn\u002Fmachine-learning-for-software-engineers) | A complete daily plan for studying to become a machine learning engineer. | None | 23326 | 5466 |\n| :arrow_up:2 | 14 | [AiLearning](https:\u002F\u002Fgithub.com\u002Fapachecn\u002FAiLearning) | AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP | Python | 22923 | 7996 |\n| :arrow_down:2 | 15 | [Detectron](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FDetectron) | FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet. | Python | 22754 | 5016 |\n| :arrow_down:1 | 16 | [awesome-deep-learning-papers](https:\u002F\u002Fgithub.com\u002Fterryum\u002Fawesome-deep-learning-papers) | The most cited deep learning papers | TeX | 20574 | 3987 |\n| :arrow_up:1 | 17 | [handson-ml](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml) | A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow. | Jupyter Notebook | 18622 | 10022 |\n| :arrow_down:1 | 18 | [incubator-mxnet](https:\u002F\u002Fgithub.com\u002Fapache\u002Fincubator-mxnet) | Lightweight, Portable, Flexible Distributed\u002FMobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more | Python | 18344 | 6528 |\n| :arrow_up:1 | 19 | [data-science-ipython-notebooks](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-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. | Python | 17947 | 5528 |\n| :arrow_up:1 | 20 | [fastai](https:\u002F\u002Fgithub.com\u002Ffastai\u002Ffastai) | The fastai deep learning library, plus lessons and tutorials | Jupyter Notebook | 17001 | 6029 |\n| :arrow_down:2 | 21 | [CNTK](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FCNTK) | Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit | C++ | 16658 | 4420 |\n| :heavy_minus_sign: | 22 | [darknet](https:\u002F\u002Fgithub.com\u002Fpjreddie\u002Fdarknet) | Convolutional Neural Networks | C | 16203 | 10402 |\n| :arrow_up:15 | 23 | [d2l-zh](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-zh) | 《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。 | Python | 15910 | 4061 |\n| :arrow_up:1 | 24 | [openpose](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose) | OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation | C++ | 15825 | 4682 |\n| :arrow_down:2 | 25 | [spaCy](https:\u002F\u002Fgithub.com\u002Fexplosion\u002FspaCy) | 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython | Python | 15643 | 2755 |\n| :heavy_minus_sign: | 26 | [Mask_RCNN](https:\u002F\u002Fgithub.com\u002Fmatterport\u002FMask_RCNN) | Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow | Python | 15583 | 7251 |\n| :arrow_up:4 | 27 | [ML-From-Scratch](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FML-From-Scratch) | Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. | Python | 15327 | 2935 |\n| :arrow_up:2 | 28 | [pytorch-tutorial](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial) | PyTorch Tutorial for Deep Learning Researchers | Python | 15314 | 4813 |\n| :arrow_up:62 | 29 | [Real-Time-Voice-Cloning](https:\u002F\u002Fgithub.com\u002FCorentinJ\u002FReal-Time-Voice-Cloning) | Clone a voice in 5 seconds to generate arbitrary speech in real-time | Python | 15014 | 2651 |\n| :arrow_down:2 | 30 | [100-Days-Of-ML-Code](https:\u002F\u002Fgithub.com\u002FMLEveryday\u002F100-Days-Of-ML-Code) | 100-Days-Of-ML-Code中文版 | Jupyter Notebook | 14977 | 4170 |\n| :arrow_down:4 | 31 | [awesome-deep-learning](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning) | A curated list of awesome Deep Learning tutorials, projects and communities. | None | 14565 | 4592 |\n| :arrow_down:8 | 32 | [lectures](https:\u002F\u002Fgithub.com\u002Foxford-cs-deepnlp-2017\u002Flectures) | Oxford Deep NLP 2017 course | None | 14411 | 3477 |\n| :arrow_down:4 | 33 | [TensorFlow-Course](https:\u002F\u002Fgithub.com\u002Fmachinelearningmindset\u002FTensorFlow-Course) | Simple and ready-to-use tutorials for TensorFlow | Python | 13938 | 2782 |\n| :arrow_down:2 | 34 | [Qix](https:\u002F\u002Fgithub.com\u002Fty4z2008\u002FQix) | Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang | None | 13091 | 4701 |\n| :arrow_down:2 | 35 | [cheatsheets-ai](https:\u002F\u002Fgithub.com\u002Fkailashahirwar\u002Fcheatsheets-ai) | Essential Cheat Sheets for deep learning and machine learning researchers https:\u002F\u002Fmedium.com\u002F@kailashahirwar\u002Fessential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5 | None | 13068 | 3175 |\n| :arrow_down:2 | 36 | [openface](https:\u002F\u002Fgithub.com\u002Fcmusatyalab\u002Fopenface) | Face recognition with deep neural networks. | Lua | 13043 | 3260 |\n| :arrow_up:2 | 37 | [DeepSpeech](https:\u002F\u002Fgithub.com\u002Fmozilla\u002FDeepSpeech) | A TensorFlow implementation of Baidu's DeepSpeech architecture | C++ | 12951 | 2417 |\n| :arrow_down:2 | 38 | [tfjs](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftfjs) | A WebGL accelerated JavaScript library for training and deploying ML models. | TypeScript | 12566 | 1040 |\n| :arrow_down:4 | 39 | [Screenshot-to-code](https:\u002F\u002Fgithub.com\u002Femilwallner\u002FScreenshot-to-code) | A neural network that transforms a design mock-up into a static website. | HTML | 12397 | 1226 |\n| :arrow_up:49 | 40 | [DeepFaceLab](https:\u002F\u002Fgithub.com\u002Fiperov\u002FDeepFaceLab) | DeepFaceLab is the leading software for creating deep fakes. | Python | 12237 | 2802 |\n| :arrow_up:13 | 41 | [deeplearning-models](https:\u002F\u002Fgithub.com\u002Frasbt\u002Fdeeplearning-models) | A collection of various deep learning architectures, models, and tips | Jupyter Notebook | 11483 | 2678 |\n| :arrow_down:5 | 42 | [deeplearning4j](https:\u002F\u002Fgithub.com\u002Feclipse\u002Fdeeplearning4j) | Eclipse Deeplearning4j, ND4J, DataVec and more - deep learning & linear algebra for Java\u002FScala with GPUs + Spark | Java | 11454 | 4803 |\n| :arrow_down:2 | 43 | [awesome-datascience](https:\u002F\u002Fgithub.com\u002Facademic\u002Fawesome-datascience) | :memo: An awesome Data Science repository to learn and apply for real world problems. | None | 10992 | 3237 |\n| :arrow_up:6 | 44 | [pytorch-CycleGAN-and-pix2pix](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002Fpytorch-CycleGAN-and-pix2pix) | Image-to-Image Translation in PyTorch | Python | 10911 | 3141 |\n| :arrow_down:5 | 45 | [pix2code](https:\u002F\u002Fgithub.com\u002Ftonybeltramelli\u002Fpix2code) | pix2code: Generating Code from a Graphical User Interface Screenshot | Python | 10709 | 1160 |\n| :arrow_down:4 | 46 | [neural-networks-and-deep-learning](https:\u002F\u002Fgithub.com\u002Fmnielsen\u002Fneural-networks-and-deep-learning) | Code samples for my book \"Neural Networks and Deep Learning\" | Python | 10687 | 5046 |\n| :arrow_down:3 | 47 | [Paddle](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPaddle) | PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署） | C++ | 10676 | 2823 |\n| :arrow_up:4 | 48 | [nndl.github.io](https:\u002F\u002Fgithub.com\u002Fnndl\u002Fnndl.github.io) | 《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning | HTML | 10517 | 2356 |\n| :arrow_up:14 | 49 | [ray](https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray) | A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. | Python | 10248 | 1484 |\n| :arrow_up:32 | 50 | [pytorch-handbook](https:\u002F\u002Fgithub.com\u002Fzergtant\u002Fpytorch-handbook) | pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行 | Jupyter Notebook | 10163 | 3056 |\n| :arrow_down:8 | 51 | [FastPhotoStyle](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FFastPhotoStyle) | Style transfer, deep learning, feature transform | Python | 10052 | 1041 |\n| :arrow_up:3 | 52 | [facenet](https:\u002F\u002Fgithub.com\u002Fdavidsandberg\u002Ffacenet) | Face recognition using Tensorflow | Python | 9965 | 4055 |\n| :arrow_down:7 | 53 | [char-rnn](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fchar-rnn) | Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch | Lua | 9953 | 2370 |\n| :arrow_down:5 | 54 | [Machine-Learning-Tutorials](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FMachine-Learning-Tutorials) | machine learning and deep learning tutorials, articles and other resources | None | 9920 | 3029 |\n| :arrow_down:10 | 55 | [convnetjs](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fconvnetjs) | Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser. | JavaScript | 9888 | 1976 |\n| :arrow_down:3 | 56 | [stanford-cs-229-machine-learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning) | VIP cheatsheets for Stanford's CS 229 Machine Learning | None | 9888 | 2402 |\n| :arrow_down:9 | 57 | [neural-enhance](https:\u002F\u002Fgithub.com\u002Falexjc\u002Fneural-enhance) | Super Resolution for images using deep learning. | Python | 9868 | 1118 |\n| :new: | 58 | [nsfw_data_scraper](https:\u002F\u002Fgithub.com\u002Falex000kim\u002Fnsfw_data_scraper) | Collection of scripts to aggregate image data for the purposes of training an NSFW Image Classifier | Shell | 9853 | 2605 |\n| :arrow_down:1 | 59 | [awesome-nlp](https:\u002F\u002Fgithub.com\u002Fkeon\u002Fawesome-nlp) | :book: A curated list of resources dedicated to Natural Language Processing (NLP) | None | 9846 | 1822 |\n| :arrow_down:13 | 60 | [dive-into-machine-learning](https:\u002F\u002Fgithub.com\u002Fhangtwenty\u002Fdive-into-machine-learning) | Dive into Machine Learning with Python Jupyter notebook and scikit-learn! | None | 9786 | 1817 |\n| :new: | 61 | [spleeter](https:\u002F\u002Fgithub.com\u002Fdeezer\u002Fspleeter) | Deezer source separation library including pretrained models. | Python | 9752 | 853 |\n| :arrow_up:6 | 62 | [labelImg](https:\u002F\u002Fgithub.com\u002Ftzutalin\u002FlabelImg) | 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images | Python | 9635 | 3282 |\n| :arrow_down:3 | 63 | [tensor2tensor](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensor2tensor) | Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. | Python | 9522 | 2456 |\n| :arrow_down:8 | 64 | [CycleGAN](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FCycleGAN) | Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more. | Lua | 9419 | 1575 |\n| :arrow_down:6 | 65 | [stanford-tensorflow-tutorials](https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Fstanford-tensorflow-tutorials) | This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research. | Python | 9377 | 4273 |\n| :arrow_down:15 | 66 | [tflearn](https:\u002F\u002Fgithub.com\u002Ftflearn\u002Ftflearn) | Deep learning library featuring a higher-level API for TensorFlow. | Python | 9363 | 2396 |\n| :arrow_up:2 | 67 | [deep-learning-with-python-notebooks](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-with-python-notebooks) | Jupyter notebooks for the code samples of the book \"Deep Learning with Python\" | Jupyter Notebook | 9349 | 4607 |\n| :arrow_down:11 | 68 | [turicreate](https:\u002F\u002Fgithub.com\u002Fapple\u002Fturicreate) | Turi Create simplifies the development of custom machine learning models. | C++ | 9331 | 949 |\n| :arrow_up:7 | 69 | [learnopencv](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv) | Learn OpenCV : C++ and Python Examples | Jupyter Notebook | 9264 | 6080 |\n| :arrow_up:1 | 70 | [DeOldify](https:\u002F\u002Fgithub.com\u002Fjantic\u002FDeOldify) | A Deep Learning based project for colorizing and restoring old images (and video!) | Jupyter Notebook | 8949 | 988 |\n| :arrow_up:2 | 71 | [Awesome-pytorch-list](https:\u002F\u002Fgithub.com\u002Fbharathgs\u002FAwesome-pytorch-list) | A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc. | None | 8917 | 1954 |\n| :arrow_down:6 | 72 | [DeepCreamPy](https:\u002F\u002Fgithub.com\u002Fdeeppomf\u002FDeepCreamPy) | Decensoring Hentai with Deep Neural Networks | Python | 8874 | 961 |\n| :arrow_down:11 | 73 | [awesome-deep-vision](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision) | A curated list of deep learning resources for computer vision | None | 8842 | 2586 |\n| :arrow_down:7 | 74 | [fast-style-transfer](https:\u002F\u002Fgithub.com\u002Flengstrom\u002Ffast-style-transfer) | TensorFlow CNN for fast style transfer ⚡🖥🎨🖼 | Python | 8667 | 2160 |\n| :arrow_down:10 | 75 | [EffectiveTensorflow](https:\u002F\u002Fgithub.com\u002Fvahidk\u002FEffectiveTensorflow) | TensorFlow 1.x and 2.x tutorials and best practices. | None | 8566 | 964 |\n| :arrow_down:15 | 76 | [tfjs-core](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftfjs-core) | WebGL-accelerated ML \u002F\u002F linear algebra \u002F\u002F automatic differentiation for JavaScript. | TypeScript | 8561 | 988 |\n| :arrow_down:5 | 77 | [dlib](https:\u002F\u002Fgithub.com\u002Fdavisking\u002Fdlib) | A toolkit for making real world machine learning and data analysis applications in C++ | C++ | 8546 | 2547 |\n| :heavy_minus_sign: | 78 | [horovod](https:\u002F\u002Fgithub.com\u002Fhorovod\u002Fhorovod) | Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. | Python | 8517 | 1330 |\n| :arrow_down:15 | 79 | [caffe2](https:\u002F\u002Fgithub.com\u002Ffacebookarchive\u002Fcaffe2) | Caffe2 is a lightweight, modular, and scalable deep learning framework. | Shell | 8482 | 2096 |\n| :arrow_down:1 | 80 | [conv_arithmetic](https:\u002F\u002Fgithub.com\u002Fvdumoulin\u002Fconv_arithmetic) | A technical report on convolution arithmetic in the context of deep learning | TeX | 8169 | 1591 |\n| :arrow_down:11 | 81 | [sonnet](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fsonnet) | TensorFlow-based neural network library | Python | 8138 | 1182 |\n| :arrow_down:2 | 82 | [ncnn](https:\u002F\u002Fgithub.com\u002FTencent\u002Fncnn) | ncnn is a high-performance neural network inference framework optimized for the mobile platform | C++ | 8071 | 2128 |\n| :arrow_up:1 | 83 | [imgaug](https:\u002F\u002Fgithub.com\u002Faleju\u002Fimgaug) | Image augmentation for machine learning experiments. | Python | 8013 | 1603 |\n| :arrow_down:9 | 84 | [TensorFlow-Tutorials](https:\u002F\u002Fgithub.com\u002FHvass-Labs\u002FTensorFlow-Tutorials) | TensorFlow Tutorials with YouTube Videos | Jupyter Notebook | 8007 | 3922 |\n| :arrow_down:8 | 85 | [libfacedetection](https:\u002F\u002Fgithub.com\u002FShiqiYu\u002Flibfacedetection) | An open source library for face detection in images. The face detection speed can reach 1500FPS. | C++ | 7971 | 2267 |\n| :arrow_down:5 | 86 | [allennlp](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fallennlp) | An open-source NLP research library, built on PyTorch. | Python | 7949 | 1707 |\n| :arrow_down:13 | 87 | [MLAlgorithms](https:\u002F\u002Fgithub.com\u002Frushter\u002FMLAlgorithms) | Minimal and clean examples of machine learning algorithms implementations | Python | 7907 | 1424 |\n| :arrow_up:5 | 88 | [netron](https:\u002F\u002Fgithub.com\u002Flutzroeder\u002Fnetron) | Visualizer for neural network, deep learning and machine learning models | JavaScript | 7882 | 959 |\n| :arrow_up:1 | 89 | [shap](https:\u002F\u002Fgithub.com\u002Fslundberg\u002Fshap) | A game theoretic approach to explain the output of any machine learning model. | Jupyter Notebook | 7792 | 1091 |\n| :arrow_down:5 | 90 | [onnx](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx) | Open Neural Network Exchange | PureBasic | 7792 | 1281 |\n| :arrow_down:4 | 91 | [ml-agents](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents) | Unity Machine Learning Agents Toolkit | Python | 7685 | 2052 |\n| :arrow_down:6 | 92 | [mit-deep-learning-book-pdf](https:\u002F\u002Fgithub.com\u002Fjanishar\u002Fmit-deep-learning-book-pdf) | MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville | Java | 7534 | 1845 |\n| :arrow_down:10 | 93 | [pix2pix](https:\u002F\u002Fgithub.com\u002Fphillipi\u002Fpix2pix) | Image-to-image translation with conditional adversarial nets | Lua | 7423 | 1289 |\n| :heavy_minus_sign: | 94 | [deep-learning-drizzle](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle) | Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! | None | 7284 | 1694 |\n| :arrow_down:7 | 95 | [fashion-mnist](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffashion-mnist) | A MNIST-like fashion product database. Benchmark :point_right: | Python | 7160 | 1564 |\n| :arrow_up:1 | 96 | [deep_learning_object_detection](https:\u002F\u002Fgithub.com\u002Fhoya012\u002Fdeep_learning_object_detection) | A paper list of object detection using deep learning. | None | 7139 | 2009 |\n| :new: | 97 | [Dive-into-DL-PyTorch](https:\u002F\u002Fgithub.com\u002FShusenTang\u002FDive-into-DL-PyTorch) | 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。 | Jupyter Notebook | 7092 | 2054 |\n| :arrow_up:2 | 98 | [mit-deep-learning](https:\u002F\u002Fgithub.com\u002Flexfridman\u002Fmit-deep-learning) | Tutorials, assignments, and competitions for MIT Deep Learning related courses. | Jupyter Notebook | 6899 | 1543 |\n| :arrow_up:25 | 99 | [recommenders](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frecommenders) | Best Practices on Recommendation Systems | Jupyter Notebook | 6899 | 977 |\n| :new: | 100 | [Deep-Learning-with-TensorFlow-book](https:\u002F\u002Fgithub.com\u002Fdragen1860\u002FDeep-Learning-with-TensorFlow-book) | 深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework. | Python | 6771 | 1848 |\n| :arrow_up:1 | 101 | [pytorch-book](https:\u002F\u002Fgithub.com\u002Fchenyuntc\u002Fpytorch-book) | PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》) | Jupyter Notebook | 6685 | 2453 |\n| :arrow_up:7 | 102 | [bert-as-service](https:\u002F\u002Fgithub.com\u002Fhanxiao\u002Fbert-as-service) | Mapping a variable-length sentence to a fixed-length vector using BERT model | Python | 6681 | 1357 |\n| :new: | 103 | [streamlit](https:\u002F\u002Fgithub.com\u002Fstreamlit\u002Fstreamlit) | Streamlit — The fastest way to build custom ML tools | Python | 6650 | 575 |\n| :arrow_down:9 | 104 | [autokeras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fautokeras) | An AutoML system based on Keras | Python | 6561 | 1058 |\n| :arrow_down:13 | 105 | [py-faster-rcnn](https:\u002F\u002Fgithub.com\u002Frbgirshick\u002Fpy-faster-rcnn) | Faster R-CNN (Python implementation) -- see https:\u002F\u002Fgithub.com\u002FShaoqingRen\u002Ffaster_rcnn for the official MATLAB version | Python | 6551 | 3875 |\n| :arrow_up:13 | 106 | [pytorch_geometric](https:\u002F\u002Fgithub.com\u002Frusty1s\u002Fpytorch_geometric) | Geometric Deep Learning Extension Library for PyTorch | Python | 6473 | 1036 |\n| :arrow_down:3 | 107 | [Keras-GAN](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FKeras-GAN) | Keras implementations of Generative Adversarial Networks. | Python | 6450 | 2323 |\n| :arrow_down:9 | 108 | [ludwig](https:\u002F\u002Fgithub.com\u002Fuber\u002Fludwig) | Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. | Python | 6350 | 724 |\n| :arrow_down:13 | 109 | [lab](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Flab) | A customisable 3D platform for agent-based AI research | C | 6052 | 1222 |\n| :arrow_down:7 | 110 | [deep-learning-models](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-models) | Keras code and weights files for popular deep learning models. | Python | 5959 | 1986 |\n| :new: | 111 | [handson-ml2](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml2) | A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. | Jupyter Notebook | 5921 | 2144 |\n| :arrow_down:6 | 112 | [tensorlayer](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayer) | Deep Learning and Reinforcement Learning Library for Scientists 🔥 | Python | 5876 | 1344 |\n| :arrow_down:15 | 113 | [BossSensor](https:\u002F\u002Fgithub.com\u002FHironsan\u002FBossSensor) | Hide screen when boss is approaching. | Python | 5830 | 1091 |\n| :arrow_down:4 | 114 | [text_classification](https:\u002F\u002Fgithub.com\u002Fbrightmart\u002Ftext_classification) | all kinds of text classification models and more with deep learning | Python | 5794 | 2200 |\n| :new: | 115 | [machine-learning-yearning-cn](https:\u002F\u002Fgithub.com\u002Fdeeplearning-ai\u002Fmachine-learning-yearning-cn) | Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著 | CSS | 5770 | 1232 |\n| :arrow_down:15 | 116 | [Swift-AI](https:\u002F\u002Fgithub.com\u002FSwift-AI\u002FSwift-AI) | The Swift machine learning library. | Swift | 5666 | 549 |\n| :arrow_up:8 | 117 | [python-machine-learning-book-2nd-edition](https:\u002F\u002Fgithub.com\u002Frasbt\u002Fpython-machine-learning-book-2nd-edition) | The \"Python Machine Learning (2nd edition)\" book code repository and info resource | Jupyter Notebook | 5569 | 2288 |\n| :arrow_down:13 | 118 | [awesome-rnn](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-rnn) | Recurrent Neural Network - A curated list of resources dedicated to RNN | None | 5559 | 1403 |\n| :arrow_down:11 | 119 | [DeepLearningFlappyBird](https:\u002F\u002Fgithub.com\u002Fyenchenlin\u002FDeepLearningFlappyBird) | Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning). | Python | 5507 | 1808 |\n| :arrow_down:13 | 120 | [SerpentAI](https:\u002F\u002Fgithub.com\u002FSerpentAI\u002FSerpentAI) | Game Agent Framework. Helping you create AIs \u002F Bots to play any game you own! | Jupyter Notebook | 5451 | 607 |\n| :arrow_down:10 | 121 | [tensorflow_cookbook](https:\u002F\u002Fgithub.com\u002Fnfmcclure\u002Ftensorflow_cookbook) | Code for Tensorflow Machine Learning Cookbook | Jupyter Notebook | 5438 | 2331 |\n| :arrow_down:4 | 122 | [AdversarialNetsPapers](https:\u002F\u002Fgithub.com\u002Fzhangqianhui\u002FAdversarialNetsPapers) | The classical paper list with code about generative adversarial nets | None | 5356 | 1824 |\n| :arrow_down:8 | 123 | [darkflow](https:\u002F\u002Fgithub.com\u002Fthtrieu\u002Fdarkflow) | Translate darknet to tensorflow. Load trained weights, retrain\u002Ffine-tune using tensorflow, export constant graph def to mobile devices | Python | 5328 | 1909 |\n| :arrow_down:12 | 124 | [deepo](https:\u002F\u002Fgithub.com\u002Fufoym\u002Fdeepo) | Set up deep learning environment in a single command line. | Python | 5308 | 654 |\n| :arrow_up:58 | 125 | [nni](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fnni) | An open source AutoML toolkit for neural architecture search, model compression and hyper-parameter tuning. | Python | 5281 | 676 |\n| :arrow_down:10 | 126 | [chainer](https:\u002F\u002Fgithub.com\u002Fchainer\u002Fchainer) | A flexible framework of neural networks for deep learning | Python | 5274 | 1369 |\n| :arrow_down:6 | 127 | [awesome-artificial-intelligence](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence) | A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers | None | 5239 | 1117 |\n| :arrow_down:5 | 128 | [tensorpack](https:\u002F\u002Fgithub.com\u002Ftensorpack\u002Ftensorpack) | A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility | Python | 5213 | 1593 |\n| :arrow_down:12 | 129 | [deep-residual-networks](https:\u002F\u002Fgithub.com\u002FKaimingHe\u002Fdeep-residual-networks) | Deep Residual Learning for Image Recognition | None | 5193 | 2041 |\n| :arrow_up:12 | 130 | [nlp-tutorial](https:\u002F\u002Fgithub.com\u002Fgraykode\u002Fnlp-tutorial) | Natural Language Processing Tutorial for Deep Learning Researchers | Jupyter Notebook | 5176 | 1387 |\n| :arrow_down:11 | 131 | [cnn-text-classification-tf](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fcnn-text-classification-tf) | Convolutional Neural Network for Text Classification in Tensorflow | Python | 5107 | 2620 |\n| :arrow_down:19 | 132 | [neuraltalk](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fneuraltalk) | NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. | Python | 5086 | 1333 |\n| :arrow_down:19 | 133 | [srez](https:\u002F\u002Fgithub.com\u002Fdavid-gpu\u002Fsrez) | Image super-resolution through deep learning | Python | 5079 | 655 |\n| :arrow_down:5 | 134 | [xlnet](https:\u002F\u002Fgithub.com\u002Fzihangdai\u002Fxlnet) | XLNet: Generalized Autoregressive Pretraining for Language Understanding | Python | 5046 | 976 |\n| :arrow_down:13 | 135 | [tiny-dnn](https:\u002F\u002Fgithub.com\u002Ftiny-dnn\u002Ftiny-dnn) | header only, dependency-free deep learning framework in C++14 | C++ | 4992 | 1284 |\n| :new: | 136 | [incubator-tvm](https:\u002F\u002Fgithub.com\u002Fapache\u002Fincubator-tvm) | Open deep learning compiler stack for cpu, gpu and specialized accelerators | Python | 4966 | 1324 |\n| :arrow_up:16 | 137 | [awesome-object-detection](https:\u002F\u002Fgithub.com\u002Famusi\u002Fawesome-object-detection) | Awesome Object Detection based on handong1587 github: https:\u002F\u002Fhandong1587.github.io\u002Fdeep_learning\u002F2015\u002F10\u002F09\u002Fobject-detection.html | None | 4914 | 1474 |\n| :arrow_up:16 | 138 | [PySyft](https:\u002F\u002Fgithub.com\u002FOpenMined\u002FPySyft) | A library for encrypted, privacy preserving machine learning | Python | 4819 | 1073 |\n| :arrow_up:11 | 139 | [wav2letter](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fwav2letter) | Facebook AI Research's Automatic Speech Recognition Toolkit | C++ | 4806 | 767 |\n| :arrow_down:4 | 140 | [deep-learning-coursera](https:\u002F\u002Fgithub.com\u002FKulbear\u002Fdeep-learning-coursera) | Deep Learning Specialization by Andrew Ng on Coursera. | Jupyter Notebook | 4773 | 3710 |\n| :arrow_down:14 | 141 | [Paddle-Lite](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPaddle-Lite) | Multi-platform high performance deep learning inference engine (『飞桨』多平台高性能深度学习预测引擎） | C++ | 4770 | 993 |\n| :arrow_down:14 | 142 | [TopDeepLearning](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTopDeepLearning) | A list of popular github projects related to deep learning | Python | 4764 | 970 |\n| :arrow_up:2 | 143 | [faster-rcnn.pytorch](https:\u002F\u002Fgithub.com\u002Fjwyang\u002Ffaster-rcnn.pytorch) | A faster pytorch implementation of faster r-cnn | Python | 4764 | 1616 |\n| :arrow_down:14 | 144 | [face_classification](https:\u002F\u002Fgithub.com\u002Foarriaga\u002Fface_classification) | Real-time face detection and emotion\u002Fgender classification using fer2013\u002Fimdb datasets with a keras CNN model and openCV. | Python | 4703 | 1388 |\n| :arrow_down:19 | 145 | [keras-js](https:\u002F\u002Fgithub.com\u002Ftranscranial\u002Fkeras-js) | Run Keras models in the browser, with GPU support using WebGL | JavaScript | 4685 | 507 |\n| :arrow_up:2 | 146 | [photoprism](https:\u002F\u002Fgithub.com\u002Fphotoprism\u002Fphotoprism) | Personal Photo Management powered by Go and Google TensorFlow | Go | 4623 | 258 |\n| :arrow_down:13 | 147 | [h2o-3](https:\u002F\u002Fgithub.com\u002Fh2oai\u002Fh2o-3) | Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc. | Java | 4580 | 1672 |\n| :arrow_down:17 | 148 | [TensorFlow-World](https:\u002F\u002Fgithub.com\u002Fastorfi\u002FTensorFlow-World) | :earth_americas: Simple and ready-to-use tutorials for TensorFlow | Python | 4468 | 426 |\n| :heavy_minus_sign: | 149 | [the-incredible-pytorch](https:\u002F\u002Fgithub.com\u002Fritchieng\u002Fthe-incredible-pytorch) | The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. | None | 4463 | 883 |\n| :new: | 150 | [mediapipe](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fmediapipe) | MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines | C++ | 4458 | 785 |\n| :arrow_down:12 | 151 | [keras-rl](https:\u002F\u002Fgithub.com\u002Fkeras-rl\u002Fkeras-rl) | Deep Reinforcement Learning for Keras. | Python | 4445 | 1146 |\n| :arrow_down:19 | 152 | [edward](https:\u002F\u002Fgithub.com\u002Fblei-lab\u002Fedward) | A probabilistic programming language in TensorFlow. Deep generative models, variational inference. | Jupyter Notebook | 4435 | 780 |\n| :arrow_down:7 | 153 | [MMdnn](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FMMdnn) | MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. | Python | 4421 | 839 |\n| :arrow_down:22 | 154 | [amazon-dsstne](https:\u002F\u002Fgithub.com\u002Famzn\u002Famazon-dsstne) | Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models | C++ | 4408 | 762 |\n| :new: | 155 | [TensorFlow-2.x-Tutorials](https:\u002F\u002Fgithub.com\u002Fdragen1860\u002FTensorFlow-2.x-Tutorials) | TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。 | Jupyter Notebook | 4348 | 1415 |\n| :new: | 156 | [albumentations](https:\u002F\u002Fgithub.com\u002Falbumentations-team\u002Falbumentations) | fast image augmentation library and easy to use wrapper around other libraries | Python | 4336 | 576 |\n| :arrow_up:9 | 157 | [Grokking-Deep-Learning](https:\u002F\u002Fgithub.com\u002Fiamtrask\u002FGrokking-Deep-Learning) | this repository accompanies the book \"Grokking Deep Learning\" | Jupyter Notebook | 4313 | 926 |\n| :arrow_down:21 | 158 | [machine-learning-mindmap](https:\u002F\u002Fgithub.com\u002Fdformoso\u002Fmachine-learning-mindmap) | A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning. | None | 4309 | 762 |\n| :arrow_down:8 | 159 | [pix2pixHD](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpix2pixHD) | Synthesizing and manipulating 2048x1024 images with conditional GANs | Python | 4299 | 867 |\n| :arrow_down:25 | 160 | [neurojs](https:\u002F\u002Fgithub.com\u002Fjanhuenermann\u002Fneurojs) | A JavaScript deep learning and reinforcement learning library. | JavaScript | 4291 | 365 |\n| :arrow_up:34 | 161 | [pandas-profiling](https:\u002F\u002Fgithub.com\u002Fpandas-profiling\u002Fpandas-profiling) | Create HTML profiling reports from pandas DataFrame objects | Python | 4290 | 588 |\n| :arrow_up:5 | 162 | [PyTorch-Tutorial](https:\u002F\u002Fgithub.com\u002FMorvanZhou\u002FPyTorch-Tutorial) | Build your neural network easy and fast | Jupyter Notebook | 4286 | 1984 |\n| :arrow_down:3 | 163 | [machine_learning_examples](https:\u002F\u002Fgithub.com\u002Flazyprogrammer\u002Fmachine_learning_examples) | A collection of machine learning examples and tutorials. | Python | 4232 | 4100 |\n| :arrow_down:20 | 164 | [Realtime_Multi-Person_Pose_Estimation](https:\u002F\u002Fgithub.com\u002FZheC\u002FRealtime_Multi-Person_Pose_Estimation) | Code repo for realtime multi-person pose estimation in CVPR'17 (Oral) | Jupyter Notebook | 4190 | 1252 |\n| :arrow_down:25 | 165 | [deeplearning-papernotes](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fdeeplearning-papernotes) | Summaries and notes on Deep Learning research papers | None | 4162 | 891 |\n| :arrow_down:23 | 166 | [sketch-code](https:\u002F\u002Fgithub.com\u002Fashnkumar\u002Fsketch-code) | Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images. | Python | 4148 | 534 |\n| :arrow_down:8 | 167 | [serving](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fserving) | A flexible, high-performance serving system for machine learning models | C++ | 4148 | 1663 |\n| :arrow_down:6 | 168 | [graph_nets](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fgraph_nets) | Build Graph Nets in Tensorflow | Python | 4127 | 612 |\n| :arrow_down:22 | 169 | [tensorspace](https:\u002F\u002Fgithub.com\u002Ftensorspace-team\u002Ftensorspace) | Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js | JavaScript | 4093 | 363 |\n| :arrow_up:15 | 170 | [book](https:\u002F\u002Fgithub.com\u002FKeKe-Li\u002Fbook) | :books: All programming languages books | None | 4071 | 1492 |\n| :new: | 171 | [seq2seq-couplet](https:\u002F\u002Fgithub.com\u002Fwb14123\u002Fseq2seq-couplet) | Play couplet with seq2seq model. 用深度学习对对联。 | Python | 4060 | 813 |\n| :arrow_up:9 | 172 | [spinningup](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fspinningup) | An educational resource to help anyone learn deep reinforcement learning. | Python | 4030 | 820 |\n| :arrow_down:32 | 173 | [DeepLearningProject](https:\u002F\u002Fgithub.com\u002FSpandan-Madan\u002FDeepLearningProject) | An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch. | HTML | 4028 | 614 |\n| :arrow_down:13 | 174 | [stanford-cs-230-deep-learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-230-deep-learning) | VIP cheatsheets for Stanford's CS 230 Deep Learning | None | 4017 | 816 |\n| :arrow_up:11 | 175 | [labelme](https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme) | Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation). | Python | 4000 | 1275 |\n| :arrow_down:18 | 176 | [vrn](https:\u002F\u002Fgithub.com\u002FAaronJackson\u002Fvrn) | :man: Code for \"Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression\" | Shell | 3951 | 659 |\n| :new: | 177 | [dgl](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fdgl) | Python package built to ease deep learning on graph, on top of existing DL frameworks. | Python | 3944 | 656 |\n| :arrow_down:26 | 178 | [learning-to-learn](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Flearning-to-learn) | Learning to Learn in TensorFlow | Python | 3934 | 587 |\n| :arrow_up:19 | 179 | [MVision](https:\u002F\u002Fgithub.com\u002FEwenwan\u002FMVision) | 机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶 | C++ | 3914 | 1699 |\n| :arrow_down:6 | 180 | [DeepPavlov](https:\u002F\u002Fgithub.com\u002Fdeepmipt\u002FDeepPavlov) | An open source library for deep learning end-to-end dialog systems and chatbots. | Python | 3912 | 720 |\n| :new: | 181 | [models](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002Fmodels) | Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型） | Python | 3910 | 1839 |\n| :arrow_down:25 | 182 | [DIGITS](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDIGITS) | Deep Learning GPU Training System | HTML | 3899 | 1386 |\n| :arrow_up:7 | 183 | [carla](https:\u002F\u002Fgithub.com\u002Fcarla-simulator\u002Fcarla) | Open-source simulator for autonomous driving research. | C++ | 3885 | 1058 |\n| :arrow_down:29 | 184 | [DeepLearningTutorials](https:\u002F\u002Fgithub.com\u002Flisa-lab\u002FDeepLearningTutorials) | Deep Learning Tutorial notes and code. See the wiki for more info. | Python | 3882 | 2137 |\n| :arrow_down:21 | 185 | [DenseNet](https:\u002F\u002Fgithub.com\u002Fliuzhuang13\u002FDenseNet) | Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award). | Lua | 3833 | 923 |\n| :arrow_down:30 | 186 | [neon](https:\u002F\u002Fgithub.com\u002FNervanaSystems\u002Fneon) | Intel® Nervana™ reference deep learning framework committed to best performance on all hardware | Python | 3821 | 847 |\n| :arrow_down:7 | 187 | [OpenNMT-py](https:\u002F\u002Fgithub.com\u002FOpenNMT\u002FOpenNMT-py) | Open Source Neural Machine Translation in PyTorch | Python | 3802 | 1453 |\n| :arrow_down:25 | 188 | [DeepLearningZeroToAll](https:\u002F\u002Fgithub.com\u002Fhunkim\u002FDeepLearningZeroToAll) | TensorFlow Basic Tutorial Labs | Jupyter Notebook | 3798 | 2394 |\n| :new: | 189 | [d2l-en](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-en) | Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface. | Python | 3790 | 976 |\n| :arrow_down:17 | 190 | [Augmentor](https:\u002F\u002Fgithub.com\u002Fmdbloice\u002FAugmentor) | Image augmentation library in Python for machine learning. | Jupyter Notebook | 3767 | 714 |\n| :arrow_down:21 | 191 | [Deep-Learning-21-Examples](https:\u002F\u002Fgithub.com\u002Fhzy46\u002FDeep-Learning-21-Examples) | 《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码 | Python | 3750 | 1637 |\n| :arrow_down:20 | 192 | [mace](https:\u002F\u002Fgithub.com\u002FXiaoMi\u002Fmace) | MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms. | C++ | 3735 | 662 |\n| :arrow_down:17 | 193 | [Practical_RL](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002FPractical_RL) | A course in reinforcement learning in the wild | Jupyter Notebook | 3716 | 1082 |\n| :arrow_down:29 | 194 | [dl-docker](https:\u002F\u002Fgithub.com\u002Ffloydhub\u002Fdl-docker) | An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.) | Python | 3706 | 823 |\n| :arrow_down:27 | 195 | [deep-learning-roadmap](https:\u002F\u002Fgithub.com\u002Fmachinelearningmindset\u002Fdeep-learning-roadmap) | :satellite: All You Need to Know About Deep Learning - A kick-starter | Python | 3680 | 565 |\n| :arrow_down:21 | 196 | [SSD-Tensorflow](https:\u002F\u002Fgithub.com\u002Fbalancap\u002FSSD-Tensorflow) | Single Shot MultiBox Detector in TensorFlow | Jupyter Notebook | 3651 | 1779 |\n| :arrow_down:28 | 197 | [MachineLearning](https:\u002F\u002Fgithub.com\u002Fwepe\u002FMachineLearning) | Basic Machine Learning and Deep Learning | Python | 3648 | 2722 |\n| :new: | 198 | [machine-learning-notes](https:\u002F\u002Fgithub.com\u002Froboticcam\u002Fmachine-learning-notes) | My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1500+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1500+页)和视频链接 | Jupyter Notebook | 3612 | 1007 |\n| :new: | 199 | [ML-NLP](https:\u002F\u002Fgithub.com\u002FNLP-LOVE\u002FML-NLP) | 此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。 | Jupyter Notebook | 3603 | 1073 |\n| :arrow_down:9 | 200 | [attention-is-all-you-need-pytorch](https:\u002F\u002Fgithub.com\u002Fjadore801120\u002Fattention-is-all-you-need-pytorch) | A PyTorch implementation of the Transformer model in \"Attention is All You Need\". | Python | 3603 | 953 |","# 顶级深度学习 GitHub 仓库\n\n以下是按星数排序的前 200 个深度学习 GitHub 仓库列表。用于 GitHub 搜索 API 的查询语句为：\n\n- `deep-learning OR CNN OR RNN OR \"convolutional neural network\" OR \"recurrent neural network\"`\n\n热门深度学习 GitHub 仓库可以在这里找到：[https:\u002F\u002Fgithub.com\u002Fmbadry1\u002FTrending-Deep-Learning](https:\u002F\u002Fgithub.com\u002Fmbadry1\u002FTrending-Deep-Learning)。\n\n日期：2020 年 2 月 2 日，对比 2019 年 1 月 9 日\n\n注：此列表将定期更新。\n\n| | Pos | Name | Description | Language | Stars | Forks |\n|--------------------|-----|---------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|--------|-------|\n| :heavy_minus_sign: | 1 | [tensorflow](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow) | An Open Source Machine Learning Framework for Everyone | C++ | 140574 | 79704 |\n| :heavy_minus_sign: | 2 | [keras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fkeras) | Deep Learning for humans | Python | 46627 | 17671 |\n| :heavy_minus_sign: | 3 | [opencv](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fopencv) | Open Source Computer Vision Library | C++ | 41817 | 32255 |\n| :arrow_up:1 | 4 | [DeepLearning-500-questions](https:\u002F\u002Fgithub.com\u002Fscutan90\u002FDeepLearning-500-questions) | 深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06 | None | 36349 | 11201 |\n| :arrow_down:1 | 5 | [TensorFlow-Examples](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples) | TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) | Jupyter Notebook | 36173 | 13657 |\n| :heavy_minus_sign: | 6 | [pytorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch) | Tensors and Dynamic neural networks in Python with strong GPU acceleration | C++ | 35719 | 8990 |\n| :heavy_minus_sign: | 7 | [caffe](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe) | Caffe: a fast open framework for deep learning. | C++ | 29775 | 18028 |\n| :arrow_up:4 | 8 | [faceswap](https:\u002F\u002Fgithub.com\u002Fdeepfakes\u002Ffaceswap) | Deepfakes Software For All | Python | 28863 | 9258 |\n| :new: | 9 | [100-Days-Of-ML-Code](https:\u002F\u002Fgithub.com\u002FAvik-Jain\u002F100-Days-Of-ML-Code) | 100 Days of ML Coding | Python | 27766 | 6943 |\n| :arrow_down:1 | 10 | [deeplearningbook-chinese](https:\u002F\u002Fgithub.com\u002Fexacity\u002Fdeeplearningbook-chinese) | Deep Learning Book Chinese Translation | TeX | 27753 | 8098 |\n| :arrow_down:1 | 11 | [Deep-Learning-Papers-Reading-Roadmap](https:\u002F\u002Fgithub.com\u002Ffloodsung\u002FDeep-Learning-Papers-Reading-Roadmap) | Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! | Python | 25457 | 5818 |\n| :new: | 12 | [practicalAI](https:\u002F\u002Fgithub.com\u002FpracticalAI\u002FpracticalAI) | 📚 A practical approach to machine learning. | Jupyter Notebook | 23437 | 4171 |\n| :arrow_down:2 | 13 | [machine-learning-for-software-engineers](https:\u002F\u002Fgithub.com\u002FZuzooVn\u002Fmachine-learning-for-software-engineers) | A complete daily plan for studying to become a machine learning engineer. | None | 23326 | 5466 |\n| :arrow_up:2 | 14 | [AiLearning](https:\u002F\u002Fgithub.com\u002Fapachecn\u002FAiLearning) | AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP | Python | 22923 | 7996 |\n| :arrow_down:2 | 15 | [Detectron](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FDetectron) | FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet. | Python | 22754 | 5016 |\n| :arrow_down:1 | 16 | [awesome-deep-learning-papers](https:\u002F\u002Fgithub.com\u002Fterryum\u002Fawesome-deep-learning-papers) | The most cited deep learning papers | TeX | 20574 | 3987 |\n| :arrow_up:1 | 17 | [handson-ml](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml) | A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow. | Jupyter Notebook | 18622 | 10022 |\n| :arrow_down:1 | 18 | [incubator-mxnet](https:\u002F\u002Fgithub.com\u002Fapache\u002Fincubator-mxnet) | Lightweight, Portable, Flexible Distributed\u002FMobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more | Python | 18344 | 6528 |\n| :arrow_up:1 | 19 | [data-science-ipython-notebooks](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-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. | Python | 17947 | 5528 |\n| :arrow_up:1 | 20 | [fastai](https:\u002F\u002Fgithub.com\u002Ffastai\u002Ffastai) | The fastai deep learning library, plus lessons and tutorials | Jupyter Notebook | 17001 | 6029 |\n| :arrow_down:2 | 21 | [CNTK](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FCNTK) | Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit | C++ | 16658 | 4420 |\n| :heavy_minus_sign: | 22 | [darknet](https:\u002F\u002Fgithub.com\u002Fpjreddie\u002Fdarknet) | Convolutional Neural Networks | C | 16203 | 10402 |\n| :arrow_up:15 | 23 | [d2l-zh](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-zh) | 《动手学深度学习》：面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。 | Python | 15910 | 4061 |\n| :arrow_up:1 | 24 | [openpose](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose) | OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation | C++ | 15825 | 4682 |\n| :arrow_down:2 | 25 | [spaCy](https:\u002F\u002Fgithub.com\u002Fexplosion\u002FspaCy) | 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython | Python | 15643 | 2755 |\n| :heavy_minus_sign: | 26 | [Mask_RCNN](https:\u002F\u002Fgithub.com\u002Fmatterport\u002FMask_RCNN) | Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow | Python | 15583 | 7251 |\n| :arrow_up:4 | 27 | [ML-From-Scratch](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FML-From-Scratch) | Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning. | Python | 15327 | 2935 |\n| :arrow_up:2 | 28 | [pytorch-tutorial](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial) | PyTorch Tutorial for Deep Learning Researchers | Python | 15314 | 4813 |\n| :arrow_up:62 | 29 | [Real-Time-Voice-Cloning](https:\u002F\u002Fgithub.com\u002FCorentinJ\u002FReal-Time-Voice-Cloning) | Clone a voice in 5 seconds to generate arbitrary speech in real-time | Python | 15014 | 2651 |\n| :arrow_down:2 | 30 | [100-Days-Of-ML-Code](https:\u002F\u002Fgithub.com\u002FMLEveryday\u002F100-Days-Of-ML-Code) | 100-Days-Of-ML-Code中文版 | Jupyter Notebook | 14977 | 4170 |\n| :arrow_down:4 | 31 | [awesome-deep-learning](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning) | A curated list of awesome Deep Learning tutorials, projects and communities. | None | 14565 | 4592 |\n| :arrow_down:8 | 32 | [lectures](https:\u002F\u002Fgithub.com\u002Foxford-cs-deepnlp-2017\u002Flectures) | Oxford Deep NLP 2017 course | None | 14411 | 3477 |\n| :arrow_down:4 | 33 | [TensorFlow-Course](https:\u002F\u002Fgithub.com\u002Fmachinelearningmindset\u002FTensorFlow-Course) | Simple and ready-to-use tutorials for TensorFlow | Python | 13938 | 2782 |\n| :arrow_down:2 | 34 | [Qix](https:\u002F\u002Fgithub.com\u002Fty4z2008\u002FQix) | Machine Learning、Deep Learning、PostgreSQL、Distributed System、Node.Js、Golang | None | 13091 | 4701 |\n| :arrow_down:2 | 35 | [cheatsheets-ai](https:\u002F\u002Fgithub.com\u002Fkailashahirwar\u002Fcheatsheets-ai) | Essential Cheat Sheets for deep learning and machine learning researchers https:\u002F\u002Fmedium.com\u002F@kailashahirwar\u002Fessential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5 | None | 13068 | 3175 |\n| :arrow_down:2 | 36 | [openface](https:\u002F\u002Fgithub.com\u002Fcmusatyalab\u002Fopenface) | Face recognition with deep neural networks. | Lua | 13043 | 3260 |\n| :arrow_up:2 | 37 | [DeepSpeech](https:\u002F\u002Fgithub.com\u002Fmozilla\u002FDeepSpeech) | A TensorFlow implementation of Baidu's DeepSpeech architecture | C++ | 12951 | 2417 |\n| :arrow_down:2 | 38 | [tfjs](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftfjs) | A WebGL accelerated JavaScript library for training and deploying ML models. | TypeScript | 12566 | 1040 |\n| :arrow_down:4 | 39 | [Screenshot-to-code](https:\u002F\u002Fgithub.com\u002Femilwallner\u002FScreenshot-to-code) | A neural network that transforms a design mock-up into a static website. | HTML | 12397 | 1226 |\n| :arrow_up:49 | 40 | [DeepFaceLab](https:\u002F\u002Fgithub.com\u002Fiperov\u002FDeepFaceLab) | DeepFaceLab is the leading software for creating deep fakes. | Python | 12237 | 2802 |\n| :arrow_up:13 | 41 | [deeplearning-models](https:\u002F\u002Fgithub.com\u002Frasbt\u002Fdeeplearning-models) | A collection of various deep learning architectures, models, and tips | Jupyter Notebook | 11483 | 2678 |\n| :arrow_down:5 | 42 | [deeplearning4j](https:\u002F\u002Fgithub.com\u002Feclipse\u002Fdeeplearning4j) | Eclipse Deeplearning4j, ND4J, DataVec and more - deep learning & linear algebra for Java\u002FScala with GPUs + Spark | Java | 11454 | 4803 |\n| :arrow_down:2 | 43 | [awesome-datascience](https:\u002F\u002Fgithub.com\u002Facademic\u002Fawesome-datascience) | :memo: An awesome Data Science repository to learn and apply for real world problems. | None | 10992 | 3237 |\n| :arrow_up:6 | 44 | [pytorch-CycleGAN-and-pix2pix](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002Fpytorch-CycleGAN-and-pix2pix) | Image-to-Image Translation in PyTorch | Python | 10911 | 3141 |\n| :arrow_down:5 | 45 | [pix2code](https:\u002F\u002Fgithub.com\u002Ftonybeltramelli\u002Fpix2code) | pix2code: Generating Code from a Graphical User Interface Screenshot | Python | 10709 | 1160 |\n| :arrow_down:4 | 46 | [neural-networks-and-deep-learning](https:\u002F\u002Fgithub.com\u002Fmnielsen\u002Fneural-networks-and-deep-learning) | Code samples for my book \"Neural Networks and Deep Learning\" | Python | 10687 | 5046 |\n| :arrow_down:3 | 47 | [Paddle](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPaddle) | PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署） | C++ | 10676 | 2823 |\n| :arrow_up:4 | 48 | [nndl.github.io](https:\u002F\u002Fgithub.com\u002Fnndl\u002Fnndl.github.io) | 《神经网络与深度学习》 邱锡鹏著 Neural Network and Deep Learning | HTML | 10517 | 2356 |\n| :arrow_up:14 | 49 | [ray](https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray) | A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. | Python | 10248 | 1484 |\n| :arrow_up:32 | 50 | [pytorch-handbook](https:\u002F\u002Fgithub.com\u002Fzergtant\u002Fpytorch-handbook) | pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行 | Jupyter Notebook | 10163 | 3056 |\n| :arrow_down:8 | 51 | [FastPhotoStyle](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FFastPhotoStyle) | Style transfer, deep learning, feature transform | Python | 10052 | 1041 |\n| :arrow_up:3 | 52 | [facenet](https:\u002F\u002Fgithub.com\u002Fdavidsandberg\u002Ffacenet) | Face recognition using Tensorflow | Python | 9965 | 4055 |\n| :arrow_down:7 | 53 | [char-rnn](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fchar-rnn) | Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch | Lua | 9953 | 2370 |\n| :arrow_down:5 | 54 | [Machine-Learning-Tutorials](https:\u002F\u002Fgithub.com\u002Fujjwalkarn\u002FMachine-Learning-Tutorials) | machine learning and deep learning tutorials, articles and other resources | None | 9920 | 3029 |\n| :arrow_down:10 | 55 | [convnetjs](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fconvnetjs) | Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser. | JavaScript | 9888 | 1976 |\n| :arrow_down:3 | 56 | [stanford-cs-229-machine-learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning) | VIP cheatsheets for Stanford's CS 229 Machine Learning | None | 9888 | 2402 |\n| :arrow_down:9 | 57 | [neural-enhance](https:\u002F\u002Fgithub.com\u002Falexjc\u002Fneural-enhance) | Super Resolution for images using deep learning. | Python | 9868 | 1118 |\n| :new: | 58 | [nsfw_data_scraper](https:\u002F\u002Fgithub.com\u002Falex000kim\u002Fnsfw_data_scraper) | Collection of scripts to aggregate image data for the purposes of training an NSFW Image Classifier | Shell | 9853 | 2605 |\n| :arrow_down:1 | 59 | [awesome-nlp](https:\u002F\u002Fgithub.com\u002Fkeon\u002Fawesome-nlp) | :book: A curated list of resources dedicated to Natural Language Processing (NLP) | None | 9846 | 1822 |\n| :arrow_down:13 | 60 | [dive-into-machine-learning](https:\u002F\u002Fgithub.com\u002Fhangtwenty\u002Fdive-into-machine-learning) | Dive into Machine Learning with Python Jupyter notebook and scikit-learn! | None | 9786 | 1817 |\n| :new: | 61 | [spleeter](https:\u002F\u002Fgithub.com\u002Fdeezer\u002Fspleeter) | Deezer source separation library including pretrained models. | Python | 9752 | 853 |\n| :arrow_up:6 | 62 | [labelImg](https:\u002F\u002Fgithub.com\u002Ftzutalin\u002FlabelImg) | 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images | Python | 9635 | 3282 |\n| :arrow_down:3 | 63 | [tensor2tensor](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensor2tensor) | Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. | Python | 9522 | 2456 |\n| :arrow_down:8 | 64 | [CycleGAN](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FCycleGAN) | Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more. | Lua | 9419 | 1575 |\n| :arrow_down:6 | 65 | [stanford-tensorflow-tutorials](https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Fstanford-tensorflow-tutorials) | This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research. | Python | 9377 | 4273 |\n| :arrow_down:15 | 66 | [tflearn](https:\u002F\u002Fgithub.com\u002Ftflearn\u002Ftflearn) | Deep learning library featuring a higher-level API for TensorFlow. | Python | 9363 | 2396 |\n| :arrow_up:2 | 67 | [deep-learning-with-python-notebooks](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-with-python-notebooks) | Jupyter notebooks for the code samples of the book \"Deep Learning with Python\" | Jupyter Notebook | 9349 | 4607 |\n| :arrow_down:11 | 68 | [turicreate](https:\u002F\u002Fgithub.com\u002Fapple\u002Fturicreate) | Turi Create simplifies the development of custom machine learning models. | C++ | 9331 | 949 |\n| :arrow_up:7 | 69 | [learnopencv](https:\u002F\u002Fgithub.com\u002Fspmallick\u002Flearnopencv) | Learn OpenCV : C++ and Python Examples | Jupyter Notebook | 9264 | 6080 |\n| :arrow_up:1 | 70 | [DeOldify](https:\u002F\u002Fgithub.com\u002Fjantic\u002FDeOldify) | A Deep Learning based project for colorizing and restoring old images (and video!) | Jupyter Notebook | 8949 | 988 |\n| :arrow_up:2 | 71 | [Awesome-pytorch-list](https:\u002F\u002Fgithub.com\u002Fbharathgs\u002FAwesome-pytorch-list) | A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc. | None | 8917 | 1954 |\n| :arrow_down:6 | 72 | [DeepCreamPy](https:\u002F\u002Fgithub.com\u002Fdeeppomf\u002FDeepCreamPy) | Decensoring Hentai with Deep Neural Networks | Python | 8874 | 961 |\n| :arrow_down:11 | 73 | [awesome-deep-vision](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-deep-vision) | A curated list of deep learning resources for computer vision | None | 8842 | 2586 |\n| :arrow_down:7 | 74 | [fast-style-transfer](https:\u002F\u002Fgithub.com\u002Flengstrom\u002Ffast-style-transfer) | TensorFlow CNN for fast style transfer ⚡🖥🎨🖼 | Python | 8667 | 2160 |\n| :arrow_down:10 | 75 | [EffectiveTensorflow](https:\u002F\u002Fgithub.com\u002Fvahidk\u002FEffectiveTensorflow) | TensorFlow 1.x and 2.x tutorials and best practices. | None | 8566 | 964 |\n| :arrow_down:15 | 76 | [tfjs-core](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftfjs-core) | WebGL-accelerated ML \u002F\u002F linear algebra \u002F\u002F automatic differentiation for JavaScript. | TypeScript | 8561 | 988 |\n| :arrow_down:5 | 77 | [dlib](https:\u002F\u002Fgithub.com\u002Fdavisking\u002Fdlib) | A toolkit for making real world machine learning and data analysis applications in C++ | C++ | 8546 | 2547 |\n| :heavy_minus_sign: | 78 | [horovod](https:\u002F\u002Fgithub.com\u002Fhorovod\u002Fhorovod) | Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. | Python | 8517 | 1330 |\n| :arrow_down:15 | 79 | [caffe2](https:\u002F\u002Fgithub.com\u002Ffacebookarchive\u002Fcaffe2) | Caffe2 is a lightweight, modular, and scalable deep learning framework. | Shell | 8482 | 2096 |\n| :arrow_down:1 | 80 | [conv_arithmetic](https:\u002F\u002Fgithub.com\u002Fvdumoulin\u002Fconv_arithmetic) | A technical report on convolution arithmetic in the context of deep learning | TeX | 8169 | 1591 |\n| :arrow_down:11 | 81 | [sonnet](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fsonnet) | TensorFlow-based neural network library | Python | 8138 | 1182 |\n| :arrow_down:2 | 82 | [ncnn](https:\u002F\u002Fgithub.com\u002FTencent\u002Fncnn) | ncnn is a high-performance neural network inference framework optimized for the mobile platform | C++ | 8071 | 2128 |\n| :arrow_up:1 | 83 | [imgaug](https:\u002F\u002Fgithub.com\u002Faleju\u002Fimgaug) | Image augmentation for machine learning experiments. | Python | 8013 | 1603 |\n| :arrow_down:9 | 84 | [TensorFlow-Tutorials](https:\u002F\u002Fgithub.com\u002FHvass-Labs\u002FTensorFlow-Tutorials) | TensorFlow Tutorials with YouTube Videos | Jupyter Notebook | 8007 | 3922 |\n| :arrow_down:8 | 85 | [libfacedetection](https:\u002F\u002Fgithub.com\u002FShiqiYu\u002Flibfacedetection) | An open source library for face detection in images. The face detection speed can reach 1500FPS. | C++ | 7971 | 2267 |\n| :arrow_down:5 | 86 | [allennlp](https:\u002F\u002Fgithub.com\u002Fallenai\u002Fallennlp) | An open-source NLP research library, built on PyTorch. | Python | 7949 | 1707 |\n| :arrow_down:13 | 87 | [MLAlgorithms](https:\u002F\u002Fgithub.com\u002Frushter\u002FMLAlgorithms) | Minimal and clean examples of machine learning algorithms implementations | Python | 7907 | 1424 |\n| :arrow_up:5 | 88 | [netron](https:\u002F\u002Fgithub.com\u002Flutzroeder\u002Fnetron) | Visualizer for neural network, deep learning and machine learning models | JavaScript | 7882 | 959 |\n| :arrow_up:1 | 89 | [shap](https:\u002F\u002Fgithub.com\u002Fslundberg\u002Fshap) | A game theoretic approach to explain the output of any machine learning model. | Jupyter Notebook | 7792 | 1091 |\n| :arrow_down:5 | 90 | [onnx](https:\u002F\u002Fgithub.com\u002Fonnx\u002Fonnx) | Open Neural Network Exchange | PureBasic | 7792 | 1281 |\n| :arrow_down:4 | 91 | [ml-agents](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents) | Unity Machine Learning Agents Toolkit | Python | 7685 | 2052 |\n| :arrow_down:6 | 92 | [mit-deep-learning-book-pdf](https:\u002F\u002Fgithub.com\u002Fjanishar\u002Fmit-deep-learning-book-pdf) | MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville | Java | 7534 | 1845 |\n| :arrow_down:10 | 93 | [pix2pix](https:\u002F\u002Fgithub.com\u002Fphillipi\u002Fpix2pix) | Image-to-image translation with conditional adversarial nets | Lua | 7423 | 1289 |\n| :heavy_minus_sign: | 94 | [deep-learning-drizzle](https:\u002F\u002Fgithub.com\u002Fkmario23\u002Fdeep-learning-drizzle) | Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! | None | 7284 | 1694 |\n| :arrow_down:7 | 95 | [fashion-mnist](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffashion-mnist) | A MNIST-like fashion product database. Benchmark :point_right: | Python | 7160 | 1564 |\n| :arrow_up:1 | 96 | [deep_learning_object_detection](https:\u002F\u002Fgithub.com\u002Fhoya012\u002Fdeep_learning_object_detection) | A paper list of object detection using deep learning. | None | 7139 | 2009 |\n| :new: | 97 | [Dive-into-DL-PyTorch](https:\u002F\u002Fgithub.com\u002FShusenTang\u002FDive-into-DL-PyTorch) | 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。 | Jupyter Notebook | 7092 | 2054 |\n| :arrow_up:2 | 98 | [mit-deep-learning](https:\u002F\u002Fgithub.com\u002Flexfridman\u002Fmit-deep-learning) | Tutorials, assignments, and competitions for MIT Deep Learning related courses. | Jupyter Notebook | 6899 | 1543 |\n| :arrow_up:25 | 99 | [recommenders](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Frecommenders) | Best Practices on Recommendation Systems | Jupyter Notebook | 6899 | 977 |\n| :new: | 100 | [Deep-Learning-with-TensorFlow-book](https:\u002F\u002Fgithub.com\u002Fdragen1860\u002FDeep-Learning-with-TensorFlow-book) | 深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework. | Python | 6771 | 1848 |\n| :arrow_up:1 | 101 | [pytorch-book](https:\u002F\u002Fgithub.com\u002Fchenyuntc\u002Fpytorch-book) | PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch：入门与实战》) | Jupyter Notebook | 6685 | 2453 |\n| :arrow_up:7 | 102 | [bert-as-service](https:\u002F\u002Fgithub.com\u002Fhanxiao\u002Fbert-as-service) | Mapping a variable-length sentence to a fixed-length vector using BERT model | Python | 6681 | 1357 |\n| :new: | 103 | [streamlit](https:\u002F\u002Fgithub.com\u002Fstreamlit\u002Fstreamlit) | Streamlit — The fastest way to build custom ML tools | Python | 6650 | 575 |\n| :arrow_down:9 | 104 | [autokeras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fautokeras) | An AutoML system based on Keras | Python | 6561 | 1058 |\n| :arrow_down:13 | 105 | [py-faster-rcnn](https:\u002F\u002Fgithub.com\u002Frbgirshick\u002Fpy-faster-rcnn) | Faster R-CNN (Python implementation) -- see https:\u002F\u002Fgithub.com\u002FShaoqingRen\u002Ffaster_rcnn for the official MATLAB version | Python | 6551 | 3875 |\n| :arrow_up:13 | 106 | [pytorch_geometric](https:\u002F\u002Fgithub.com\u002Frusty1s\u002Fpytorch_geometric) | Geometric Deep Learning Extension Library for PyTorch | Python | 6473 | 1036 |\n| :arrow_down:3 | 107 | [Keras-GAN](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FKeras-GAN) | Keras implementations of Generative Adversarial Networks. | Python | 6450 | 2323 |\n| :arrow_down:9 | 108 | [ludwig](https:\u002F\u002Fgithub.com\u002Fuber\u002Fludwig) | Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. | Python | 6350 | 724 |\n| :arrow_down:13 | 109 | [lab](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Flab) | A customisable 3D platform for agent-based AI research | C | 6052 | 1222 |\n| :arrow_down:7 | 110 | [deep-learning-models](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-models) | Keras code and weights files for popular deep learning models. | Python | 5959 | 1986 |\n| :new: | 111 | [handson-ml2](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml2) | A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. | Jupyter Notebook | 5921 | 2144 |\n| :arrow_down:6 | 112 | [tensorlayer](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayer) | Deep Learning and Reinforcement Learning Library for Scientists 🔥 | Python | 5876 | 1344 |\n| :arrow_down:15 | 113 | [BossSensor](https:\u002F\u002Fgithub.com\u002FHironsan\u002FBossSensor) | Hide screen when boss is approaching. | Python | 5830 | 1091 |\n| :arrow_down:4 | 114 | [text_classification](https:\u002F\u002Fgithub.com\u002Fbrightmart\u002Ftext_classification) | all kinds of text classification models and more with deep learning | Python | 5794 | 2200 |\n| :new: | 115 | [machine-learning-yearning-cn](https:\u002F\u002Fgithub.com\u002Fdeeplearning-ai\u002Fmachine-learning-yearning-cn) | Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著 | CSS | 5770 | 1232 |\n| :arrow_down:15 | 116 | [Swift-AI](https:\u002F\u002Fgithub.com\u002FSwift-AI\u002FSwift-AI) | The Swift machine learning library. | Swift | 5666 | 549 |\n| :arrow_up:8 | 117 | [python-machine-learning-book-2nd-edition](https:\u002F\u002Fgithub.com\u002Frasbt\u002Fpython-machine-learning-book-2nd-edition) | The \"Python Machine Learning (2nd edition)\" book code repository and info resource | Jupyter Notebook | 5569 | 2288 |\n| :arrow_down:13 | 118 | [awesome-rnn](https:\u002F\u002Fgithub.com\u002Fkjw0612\u002Fawesome-rnn) | Recurrent Neural Network - A curated list of resources dedicated to RNN | None | 5559 | 1403 |\n| :arrow_down:11 | 119 | [DeepLearningFlappyBird](https:\u002F\u002Fgithub.com\u002Fyenchenlin\u002FDeepLearningFlappyBird) | Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning). | Python | 5507 | 1808 |\n| :arrow_down:13 | 120 | [SerpentAI](https:\u002F\u002Fgithub.com\u002FSerpentAI\u002FSerpentAI) | Game Agent Framework. Helping you create AIs \u002F Bots to play any game you own! | Jupyter Notebook | 5451 | 607 |\n| :arrow_down:10 | 121 | [tensorflow_cookbook](https:\u002F\u002Fgithub.com\u002Fnfmcclure\u002Ftensorflow_cookbook) | Code for Tensorflow Machine Learning Cookbook | Jupyter Notebook | 5438 | 2331 |\n| :arrow_down:4 | 122 | [AdversarialNetsPapers](https:\u002F\u002Fgithub.com\u002Fzhangqianhui\u002FAdversarialNetsPapers) | The classical paper list with code about generative adversarial nets | None | 5356 | 1824 |\n| :arrow_down:8 | 123 | [darkflow](https:\u002F\u002Fgithub.com\u002Fthtrieu\u002Fdarkflow) | Translate darknet to tensorflow. Load trained weights, retrain\u002Ffine-tune using tensorflow, export constant graph def to mobile devices | Python | 5328 | 1909 |\n| :arrow_down:12 | 124 | [deepo](https:\u002F\u002Fgithub.com\u002Fufoym\u002Fdeepo) | Set up deep learning environment in a single command line. | Python | 5308 | 654 |\n| :arrow_up:58 | 125 | [nni](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fnni) | An open source AutoML toolkit for neural architecture search, model compression and hyper-parameter tuning. | Python | 5281 | 676 |\n| :arrow_down:10 | 126 | [chainer](https:\u002F\u002Fgithub.com\u002Fchainer\u002Fchainer) | A flexible framework of neural networks for deep learning | Python | 5274 | 1369 |\n| :arrow_down:6 | 127 | [awesome-artificial-intelligence](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence) | A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers | None | 5239 | 1117 |\n| :arrow_down:5 | 128 | [tensorpack](https:\u002F\u002Fgithub.com\u002Ftensorpack\u002Ftensorpack) | A Neural Net Training Interface on TensorFlow, with focus on speed + flexibility | Python | 5213 | 1593 |\n| :arrow_down:12 | 129 | [deep-residual-networks](https:\u002F\u002Fgithub.com\u002FKaimingHe\u002Fdeep-residual-networks) | Deep Residual Learning for Image Recognition | None | 5193 | 2041 |\n| :arrow_up:12 | 130 | [nlp-tutorial](https:\u002F\u002Fgithub.com\u002Fgraykode\u002Fnlp-tutorial) | Natural Language Processing Tutorial for Deep Learning Researchers | Jupyter Notebook | 5176 | 1387 |\n| :arrow_down:11 | 131 | [cnn-text-classification-tf](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fcnn-text-classification-tf) | Convolutional Neural Network for Text Classification in Tensorflow | Python | 5107 | 2620 |\n| :arrow_down:19 | 132 | [neuraltalk](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fneuraltalk) | NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. | Python | 5086 | 1333 |\n| :arrow_down:19 | 133 | [srez](https:\u002F\u002Fgithub.com\u002Fdavid-gpu\u002Fsrez) | Image super-resolution through deep learning | Python | 5079 | 655 |\n| :arrow_down:5 | 134 | [xlnet](https:\u002F\u002Fgithub.com\u002Fzihangdai\u002Fxlnet) | XLNet: Generalized Autoregressive Pretraining for Language Understanding | Python | 5046 | 976 |\n| :arrow_down:13 | 135 | [tiny-dnn](https:\u002F\u002Fgithub.com\u002Ftiny-dnn\u002Ftiny-dnn) | header only, dependency-free deep learning framework in C++14 | C++ | 4992 | 1284 |\n| :new: | 136 | [incubator-tvm](https:\u002F\u002Fgithub.com\u002Fapache\u002Fincubator-tvm) | Open deep learning compiler stack for cpu, gpu and specialized accelerators | Python | 4966 | 1324 |\n| :arrow_up:16 | 137 | [awesome-object-detection](https:\u002F\u002Fgithub.com\u002Famusi\u002Fawesome-object-detection) | Awesome Object Detection based on handong1587 github: https:\u002F\u002Fhandong1587.github.io\u002Fdeep_learning\u002F2015\u002F10\u002F09\u002Fobject-detection.html | None | 4914 | 1474 |\n| :arrow_up:16 | 138 | [PySyft](https:\u002F\u002Fgithub.com\u002FOpenMined\u002FPySyft) | A library for encrypted, privacy preserving machine learning | Python | 4819 | 1073 |\n| :arrow_up:11 | 139 | [wav2letter](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fwav2letter) | Facebook AI Research's Automatic Speech Recognition Toolkit | C++ | 4806 | 767 |\n| :arrow_down:4 | 140 | [deep-learning-coursera](https:\u002F\u002Fgithub.com\u002FKulbear\u002Fdeep-learning-coursera) | Deep Learning Specialization by Andrew Ng on Coursera. | Jupyter Notebook | 4773 | 3710 |\n| :arrow_down:14 | 141 | [Paddle-Lite](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002FPaddle-Lite) | Multi-platform high performance deep learning inference engine (『飞桨』多平台高性能深度学习预测引擎） | C++ | 4770 | 993 |\n| :arrow_down:14 | 142 | [TopDeepLearning](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTopDeepLearning) | A list of popular github projects related to deep learning | Python | 4764 | 970 |\n| :arrow_up:2 | 143 | [faster-rcnn.pytorch](https:\u002F\u002Fgithub.com\u002Fjwyang\u002Ffaster-rcnn.pytorch) | A faster pytorch implementation of faster r-cnn | Python | 4764 | 1616 |\n| :arrow_down:14 | 144 | [face_classification](https:\u002F\u002Fgithub.com\u002Foarriaga\u002Fface_classification) | Real-time face detection and emotion\u002Fgender classification using fer2013\u002Fimdb datasets with a keras CNN model and openCV. | Python | 4703 | 1388 |\n| :arrow_down:19 | 145 | [keras-js](https:\u002F\u002Fgithub.com\u002Ftranscranial\u002Fkeras-js) | Run Keras models in the browser, with GPU support using WebGL | JavaScript | 4685 | 507 |\n| :arrow_up:2 | 146 | [photoprism](https:\u002F\u002Fgithub.com\u002Fphotoprism\u002Fphotoprism) | Personal Photo Management powered by Go and Google TensorFlow | Go | 4623 | 258 |\n| :arrow_down:13 | 147 | [h2o-3](https:\u002F\u002Fgithub.com\u002Fh2oai\u002Fh2o-3) | Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc. | Java | 4580 | 1672 |\n| :arrow_down:17 | 148 | [TensorFlow-World](https:\u002F\u002Fgithub.com\u002Fastorfi\u002FTensorFlow-World) | :earth_americas: Simple and ready-to-use tutorials for TensorFlow | Python | 4468 | 426 |\n| :heavy_minus_sign: | 149 | [the-incredible-pytorch](https:\u002F\u002Fgithub.com\u002Fritchieng\u002Fthe-incredible-pytorch) | The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. | None | 4463 | 883 |\n| :new: | 150 | [mediapipe](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fmediapipe) | MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines | C++ | 4458 | 785 |\n| :arrow_down:12 | 151 | [keras-rl](https:\u002F\u002Fgithub.com\u002Fkeras-rl\u002Fkeras-rl) | Deep Reinforcement Learning for Keras. | Python | 4445 | 1146 |\n| :arrow_down:19 | 152 | [edward](https:\u002F\u002Fgithub.com\u002Fblei-lab\u002Fedward) | A probabilistic programming language in TensorFlow. Deep generative models, variational inference. | Jupyter Notebook | 4435 | 780 |\n| :arrow_down:7 | 153 | [MMdnn](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FMMdnn) | MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. | Python | 4421 | 839 |\n| :arrow_down:22 | 154 | [amazon-dsstne](https:\u002F\u002Fgithub.com\u002Famzn\u002Famazon-dsstne) | Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models | C++ | 4408 | 762 |\n| :new: | 155 | [TensorFlow-2.x-Tutorials](https:\u002F\u002Fgithub.com\u002Fdragen1860\u002FTensorFlow-2.x-Tutorials) | TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。 | Jupyter Notebook | 4348 | 1415 |\n| :new: | 156 | [albumentations](https:\u002F\u002Fgithub.com\u002Falbumentations-team\u002Falbumentations) | fast image augmentation library and easy to use wrapper around other libraries | Python | 4336 | 576 |\n| :arrow_up:9 | 157 | [Grokking-Deep-Learning](https:\u002F\u002Fgithub.com\u002Fiamtrask\u002FGrokking-Deep-Learning) | this repository accompanies the book \"Grokking Deep Learning\" | Jupyter Notebook | 4313 | 926 |\n| :arrow_down:21 | 158 | [machine-learning-mindmap](https:\u002F\u002Fgithub.com\u002Fdformoso\u002Fmachine-learning-mindmap) | A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning. | None | 4309 | 762 |\n| :arrow_down:8 | 159 | [pix2pixHD](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpix2pixHD) | Synthesizing and manipulating 2048x1024 images with conditional GANs | Python | 4299 | 867 |\n| :arrow_down:25 | 160 | [neurojs](https:\u002F\u002Fgithub.com\u002Fjanhuenermann\u002Fneurojs) | A JavaScript deep learning and reinforcement learning library. | JavaScript | 4291 | 365 |\n| :arrow_up:34 | 161 | [pandas-profiling](https:\u002F\u002Fgithub.com\u002Fpandas-profiling\u002Fpandas-profiling) | Create HTML profiling reports from pandas DataFrame objects | Python | 4290 | 588 |\n| :arrow_up:5 | 162 | [PyTorch-Tutorial](https:\u002F\u002Fgithub.com\u002FMorvanZhou\u002FPyTorch-Tutorial) | Build your neural network easy and fast | Jupyter Notebook | 4286 | 1984 |\n| :arrow_down:3 | 163 | [machine_learning_examples](https:\u002F\u002Fgithub.com\u002Flazyprogrammer\u002Fmachine_learning_examples) | A collection of machine learning examples and tutorials. | Python | 4232 | 4100 |\n| :arrow_down:20 | 164 | [Realtime_Multi-Person_Pose_Estimation](https:\u002F\u002Fgithub.com\u002FZheC\u002FRealtime_Multi-Person_Pose_Estimation) | Code repo for realtime multi-person pose estimation in CVPR'17 (Oral) | Jupyter Notebook | 4190 | 1252 |\n| :arrow_down:25 | 165 | [deeplearning-papernotes](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fdeeplearning-papernotes) | Summaries and notes on Deep Learning research papers | None | 4162 | 891 |\n| :arrow_down:23 | 166 | [sketch-code](https:\u002F\u002Fgithub.com\u002Fashnkumar\u002Fsketch-code) | Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images. | Python | 4148 | 534 |\n| :arrow_down:8 | 167 | [serving](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fserving) | A flexible, high-performance serving system for machine learning models | C++ | 4148 | 1663 |\n| :arrow_down:6 | 168 | [graph_nets](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fgraph_nets) | Build Graph Nets in Tensorflow | Python | 4127 | 612 |\n| :arrow_down:22 | 169 | [tensorspace](https:\u002F\u002Fgithub.com\u002Ftensorspace-team\u002Ftensorspace) | Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js | JavaScript | 4093 | 363 |\n| :arrow_up:15 | 170 | [book](https:\u002F\u002Fgithub.com\u002FKeKe-Li\u002Fbook) | :books: All programming languages books | None | 4071 | 1492 |\n| :new: | 171 | [seq2seq-couplet](https:\u002F\u002Fgithub.com\u002Fwb14123\u002Fseq2seq-couplet) | Play couplet with seq2seq model. 用深度学习对对联。 | Python | 4060 | 813 |\n| :arrow_up:9 | 172 | [spinningup](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fspinningup) | An educational resource to help anyone learn deep reinforcement learning. | Python | 4030 | 820 |\n| :arrow_down:32 | 173 | [DeepLearningProject](https:\u002F\u002Fgithub.com\u002FSpandan-Madan\u002FDeepLearningProject) | An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch. | HTML | 4028 | 614 |\n| :arrow_down:13 | 174 | [stanford-cs-230-deep-learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-230-deep-learning) | VIP cheatsheets for Stanford's CS 230 Deep Learning | None | 4017 | 816 |\n| :arrow_up:11 | 175 | [labelme](https:\u002F\u002Fgithub.com\u002Fwkentaro\u002Flabelme) | Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation). | Python | 4000 | 1275 |\n| :arrow_down:18 | 176 | [vrn](https:\u002F\u002Fgithub.com\u002FAaronJackson\u002Fvrn) | :man: Code for \"Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression\" | Shell | 3951 | 659 |\n| :new: | 177 | [dgl](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fdgl) | Python package built to ease deep learning on graph, on top of existing DL frameworks. | Python | 3944 | 656 |\n| :arrow_down:26 | 178 | [learning-to-learn](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Flearning-to-learn) | Learning to Learn in TensorFlow | Python | 3934 | 587 |\n| :arrow_up:19 | 179 | [MVision](https:\u002F\u002Fgithub.com\u002FEwenwan\u002FMVision) | 机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶 | C++ | 3914 | 1699 |\n| :arrow_down:6 | 180 | [DeepPavlov](https:\u002F\u002Fgithub.com\u002Fdeepmipt\u002FDeepPavlov) | An open source library for deep learning end-to-end dialog systems and chatbots. | Python | 3912 | 720 |\n| :new: | 181 | [models](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002Fmodels) | Pre-trained and Reproduced Deep Learning Models （『飞桨』官方模型库，包含多种学术前沿和工业场景验证的深度学习模型） | Python | 3910 | 1839 |\n| :arrow_down:25 | 182 | [DIGITS](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDIGITS) | Deep Learning GPU Training System | HTML | 3899 | 1386 |\n| :arrow_up:7 | 183 | [carla](https:\u002F\u002Fgithub.com\u002Fcarla-simulator\u002Fcarla) | Open-source simulator for autonomous driving research. | C++ | 3885 | 1058 |\n| :arrow_down:29 | 184 | [DeepLearningTutorials](https:\u002F\u002Fgithub.com\u002Flisa-lab\u002FDeepLearningTutorials) | Deep Learning Tutorial notes and code. See the wiki for more info. | Python | 3882 | 2137 |\n| :arrow_down:21 | 185 | [DenseNet](https:\u002F\u002Fgithub.com\u002Fliuzhuang13\u002FDenseNet) | Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award). | Lua | 3833 | 923 |\n| :arrow_down:30 | 186 | [neon](https:\u002F\u002Fgithub.com\u002FNervanaSystems\u002Fneon) | Intel® Nervana™ reference deep learning framework committed to best performance on all hardware | Python | 3821 | 847 |\n| :arrow_down:7 | 187 | [OpenNMT-py](https:\u002F\u002Fgithub.com\u002FOpenNMT\u002FOpenNMT-py) | Open Source Neural Machine Translation in PyTorch | Python | 3802 | 1453 |\n| :arrow_down:25 | 188 | [DeepLearningZeroToAll](https:\u002F\u002Fgithub.com\u002Fhunkim\u002FDeepLearningZeroToAll) | TensorFlow Basic Tutorial Labs | Jupyter Notebook | 3798 | 2394 |\n| :new: | 189 | [d2l-en](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-en) | Dive into Deep Learning: an interactive deep learning book with code, math, and discussions, based on the NumPy interface. | Python | 3790 | 976 |\n| :arrow_down:17 | 190 | [Augmentor](https:\u002F\u002Fgithub.com\u002Fmdbloice\u002FAugmentor) | Image augmentation library in Python for machine learning. | Jupyter Notebook | 3767 | 714 |\n| :arrow_down:21 | 191 | [Deep-Learning-21-Examples](https:\u002F\u002Fgithub.com\u002Fhzy46\u002FDeep-Learning-21-Examples) | 《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码 | Python | 3750 | 1637 |\n| :arrow_down:20 | 192 | [mace](https:\u002F\u002Fgithub.com\u002FXiaoMi\u002Fmace) | MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms. | C++ | 3735 | 662 |\n| :arrow_down:17 | 193 | [Practical_RL](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002FPractical_RL) | A course in reinforcement learning in the wild | Jupyter Notebook | 3716 | 1082 |\n| :arrow_down:29 | 194 | [dl-docker](https:\u002F\u002Fgithub.com\u002Ffloydhub\u002Fdl-docker) | An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.) | Python | 3706 | 823 |\n| :arrow_down:27 | 195 | [deep-learning-roadmap](https:\u002F\u002Fgithub.com\u002Fmachinelearningmindset\u002Fdeep-learning-roadmap) | :satellite: All You Need to Know About Deep Learning - A kick-starter | Python | 3680 | 565 |\n| :arrow_down:21 | 196 | [SSD-Tensorflow](https:\u002F\u002Fgithub.com\u002Fbalancap\u002FSSD-Tensorflow) | Single Shot MultiBox Detector in TensorFlow | Jupyter Notebook | 3651 | 1779 |\n| :arrow_down:28 | 197 | [MachineLearning](https:\u002F\u002Fgithub.com\u002Fwepe\u002FMachineLearning) | Basic Machine Learning and Deep Learning | Python | 3648 | 2722 |\n| :new: | 198 | [machine-learning-notes](https:\u002F\u002Fgithub.com\u002Froboticcam\u002Fmachine-learning-notes) | My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1500+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1500+页)和视频链接 | Jupyter Notebook | 3612 | 1007 |\n| :new: | 199 | [ML-NLP](https:\u002F\u002Fgithub.com\u002FNLP-LOVE\u002FML-NLP) | 此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。 | Jupyter Notebook | 3603 | 1073 |\n| :arrow_down:9 | 200 | [attention-is-all-you-need-pytorch](https:\u002F\u002Fgithub.com\u002Fjadore801120\u002Fattention-is-all-you-need-pytorch) | A PyTorch implementation of the Transformer model in \"Attention is All You Need\". | Python | 3603 | 953 |","# Top-Deep-Learning 快速上手指南\n\n**工具简介**：\n`Top-Deep-Learning` 并非一个可安装的软件库或框架，而是一个**精选资源列表**。它汇总了 GitHub 上按星标数排序的前 200 个深度学习开源项目（如 TensorFlow, PyTorch, Keras, OpenCV 等）。本指南旨在帮助开发者如何利用该列表快速定位并上手其中排名靠前的核心工具。\n\n---\n\n## 1. 环境准备\n\n由于该列表包含多种不同技术栈的项目（C++, Python, Jupyter Notebook 等），请根据你感兴趣的具体项目准备环境。以下是针对列表中大多数主流项目（如 TensorFlow, PyTorch, Keras）的通用推荐配置：\n\n* **操作系统**：Linux (Ubuntu 18.04+ 推荐), macOS, 或 Windows 10\u002F11 (建议配合 WSL2)\n* **编程语言**：Python 3.7 - 3.10\n* **硬件要求**：\n * CPU：多核处理器\n * GPU（可选但推荐）：NVIDIA GPU (显存 4GB+)，需安装对应的 CUDA Toolkit 和 cuDNN\n* **前置依赖**：\n * Git\n * pip 或 conda (推荐使用 Anaconda\u002FMiniconda 管理环境)\n\n**国内加速建议**：\n* **Git 克隆加速**：若 `github.com` 访问缓慢，可使用 Gitee 镜像或配置代理。\n* **Python 包下载**：使用清华源或阿里源加速 pip 安装。\n ```bash\n pip config set global.index-url https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n ```\n\n---\n\n## 2. 获取与安装步骤\n\n由于这是一个资源列表，\"安装\"实际上是指**克隆你感兴趣的具体仓库**。以下以列表中排名第 1 的 `tensorflow` 和第 6 的 `pytorch` 为例，演示如何获取并开始学习。\n\n### 步骤一：浏览列表选择项目\n访问 [Top-Deep-Learning GitHub 仓库](https:\u002F\u002Fgithub.com\u002Fmbadry1\u002FTop-Deep-Learning) 查看完整榜单，找到你需要的工具名称（例如 `tensorflow`, `keras`, `faceswap` 等）。\n\n### 步骤二：克隆目标仓库\n使用 Git 将选中的项目克隆到本地。\n\n```bash\n# 示例：克隆排名第一的 TensorFlow\ngit clone https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow.git\n\n# 示例：克隆排名第六的 PyTorch\ngit clone https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch.git\n\n# 示例：克隆适合新手的教程项目 (排名第 5)\ngit clone https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples.git\n```\n\n> **提示**：如果直接克隆速度慢，可将 `github.com` 替换为国内镜像地址（如 `gitee.com` 上对应的镜像仓库）。\n\n### 步骤三：安装项目依赖\n进入克隆后的目录，通常需要根据该项目具体的 `requirements.txt` 或官方文档安装依赖。\n\n```bash\n# 进入示例项目目录\ncd TensorFlow-Examples\n\n# 创建虚拟环境 (推荐)\npython -m venv venv\nsource venv\u002Fbin\u002Factivate # Windows 用户使用: venv\\Scripts\\activate\n\n# 安装依赖 (具体文件名视项目而定，常见为 requirements.txt)\npip install -r requirements.txt\n```\n\n*注意：对于大型框架如 `tensorflow` 或 `pytorch` 源码，通常不建议直接从源码编译安装，而是通过 pip 安装其预编译版本进行学习和使用：*\n\n```bash\n# 安装 TensorFlow (CPU 版)\npip install tensorflow\n\n# 安装 PyTorch (根据官网命令生成，此处为通用 CPU 版示例)\npip install torch torchvision torchaudio\n```\n\n---\n\n## 3. 基本使用\n\n以最实用的入门教程项目 **[TensorFlow-Examples](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples)** (列表第 5 名) 为例，演示如何运行一个简单的深度学习示例。\n\n### 运行第一个示例\n该项目包含了大量独立的 Jupyter Notebook 和 Python 脚本。\n\n1. **启动 Jupyter Notebook** (如果项目包含 `.ipynb` 文件)：\n ```bash\n jupyter notebook\n ```\n 在浏览器中打开 `basic_operations.ipynb` 或 `convolutional_network.ipynb` 逐步运行代码块。\n\n2. **直接运行 Python 脚本**：\n 选择一个简单的脚本，例如线性回归示例：\n\n ```bash\n python basic_operations.py\n ```\n\n 或者运行一个卷积神经网络 (CNN) 示例（需确保已下载对应数据集，脚本通常会自动下载）：\n\n ```bash\n python convolutional_network.py\n ```\n\n### 预期输出\n终端将显示训练过程日志，包括 Loss 值和准确率，类似于：\n```text\nStep 1, Minibatch Loss= 1.8543, Training Accuracy= 0.45\nStep 100, Minibatch Loss= 0.4211, Training Accuracy= 0.89\n...\nOptimization Finished!\n```\n\n---\n\n**下一步建议**：\n回到 `Top-Deep-Learning` 列表，根据你的需求探索其他高星项目：\n* 想学习理论？阅读 `DeepLearning-500-questions` 或 `deeplearningbook-chinese`。\n* 想做计算机视觉？研究 `opencv` 或 `Detectron`。\n* 想实战换脸？尝试 `faceswap`。","某初创公司的算法团队正计划开发一套基于计算机视觉的工业缺陷检测系统，急需在两周内确定技术栈并找到高质量的开源代码作为基线。\n\n### 没有 Top-Deep-Learning 时\n- **筛选效率极低**：工程师需在 GitHub 搜索\"deep learning\"或\"CNN\"等关键词，面对数万个结果逐页翻阅，耗费大量时间辨别项目质量。\n- **难以评估成熟度**：缺乏统一的星级排序参考，容易误选星数少、维护停滞的“僵尸项目”，导致后期集成困难或被迫重构。\n- **技术视野受限**：团队仅关注熟悉的框架（如 TensorFlow），可能错过列表中像 `DeepLearning-500-questions` 这样高星的中文学习资料或其他语言实现的优秀库，错失更优解决方案。\n- **决策依据模糊**：在技术选型会议上，成员只能凭个人经验推荐库，缺乏客观数据（如 Star 数、Fork 数对比）支撑，导致争论不休，拖延项目进度。\n\n### 使用 Top-Deep-Learning 后\n- **快速锁定目标**：直接查看按星数排序的前 200 个仓库，几分钟内即可锁定 `tensorflow`、`pytorch` 和 `opencv` 等经过社区验证的顶级框架。\n- **规避试错风险**：依托榜单中的 Star\u002FFork 数据及更新时间，团队迅速排除了低活跃度项目，确保选用的基线代码稳定可靠且社区支持强大。\n- **发现隐藏宝藏**：通过榜单意外发现了排名靠前的中文教程和高星示例项目（如 `TensorFlow-Examples`），大幅降低了团队成员的学习成本和文档查阅时间。\n- **高效达成共识**：利用榜单提供的客观排名数据，团队迅速统一了以 PyTorch 为核心、OpenCV 为预处理的技术路线，将选型会议时间从半天缩短至半小时。\n\nTop-Deep-Learning 通过将海量开源项目量化排序，帮助开发者在信息过载的 GitHub 中瞬间识别出最具价值的深度学习资源，极大提升了技术选型与研发启动的效率。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmbadry1_Top-Deep-Learning_bb7ae35e.png","mbadry1","Mahmoud Badry","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fmbadry1_d5dcada2.png","A deep learning researcher, a .NET developer, and a good learner.",null,"Cairo, Egypt","mahmoud.badry100@yahoo.com","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fmbadry1\u002F","https:\u002F\u002Fgithub.com\u002Fmbadry1",[85],{"name":86,"color":87,"percentage":88},"Python","#3572A5",100,1763,438,"2026-04-02T08:36:54","MIT",1,"","未说明",{"notes":97,"python":95,"dependencies":98},"该仓库并非一个可运行的 AI 工具或框架，而是一份截至 2020 年 2 月 2 日的深度学习领域热门 GitHub 仓库排行榜（列表）。它仅包含对其他项目（如 TensorFlow, PyTorch, Keras 等）的引用和描述，本身不包含代码、模型或安装脚本，因此没有特定的运行环境、依赖库或硬件需求。用户若需使用列表中提及的具体工具，请参考对应项目的官方文档。",[],[13],[101,102,103,104,105,106,107,108,109,110],"deep-learning","deep-neural-networks","deep-reinforcement-learning","convolutional-neural-networks","recurrent-neural-networks","stargazers-count","artificial-neural-networks","artificial-intelligence","machine-learning","top-repositories","2026-03-27T02:49:30.150509","2026-04-06T05:17:37.331530",[],[]]