[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-mbadry1--Trending-Deep-Learning":3,"tool-mbadry1--Trending-Deep-Learning":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",160015,2,"2026-04-18T11:30:52",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",109154,"2026-04-18T11:18:24",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":77,"owner_location":78,"owner_email":79,"owner_twitter":77,"owner_website":80,"owner_url":81,"languages":82,"stars":87,"forks":88,"last_commit_at":89,"license":90,"difficulty_score":91,"env_os":92,"env_gpu":93,"env_ram":93,"env_deps":94,"category_tags":97,"github_topics":98,"view_count":32,"oss_zip_url":77,"oss_zip_packed_at":77,"status":17,"created_at":109,"updated_at":110,"faqs":111,"releases":112},9173,"mbadry1\u002FTrending-Deep-Learning","Trending-Deep-Learning","Top 100 trending deep learning repositories sorted by the number of stars gained on a specific day.","Trending-Deep-Learning 是一个专注于深度学习领域的 GitHub 仓库热度追踪清单。它每日自动筛选并列出当天获得星标（Star）增长最多的前 100 个开源项目，帮助开发者快速捕捉社区前沿动态。\n\n在深度学习技术迭代极快的背景下，研究人员和工程师往往难以从海量代码库中及时识别出真正具有潜力或突发热点的新工具。Trending-Deep-Learning 通过量化“单日星标增量”这一指标，有效过滤了仅靠历史积累维持高关注度的老牌项目，让那些刚刚发布却极具创新价值的资源（如强化学习教程、实时语音克隆、3D 深度学习组件等）能够迅速进入大众视野。此外，该清单特意排除了星标超过 4 万的超大型仓库，确保榜单更聚焦于成长型的新兴项目。\n\n这份清单非常适合 AI 开发者、算法研究员以及希望紧跟技术趋势的学生使用。无论是寻找最新的研究代码复现方案，还是探索 TensorFlow 2.0 或 PyTorch 的实战案例，用户都能在此发现灵感。其核心亮点在于利用 GitHub 搜索 API 精准锁定包含 CNN、RNN 等关键词的项目，并以直观的表格形式展示项目名称、简介、编程语言","Trending-Deep-Learning 是一个专注于深度学习领域的 GitHub 仓库热度追踪清单。它每日自动筛选并列出当天获得星标（Star）增长最多的前 100 个开源项目，帮助开发者快速捕捉社区前沿动态。\n\n在深度学习技术迭代极快的背景下，研究人员和工程师往往难以从海量代码库中及时识别出真正具有潜力或突发热点的新工具。Trending-Deep-Learning 通过量化“单日星标增量”这一指标，有效过滤了仅靠历史积累维持高关注度的老牌项目，让那些刚刚发布却极具创新价值的资源（如强化学习教程、实时语音克隆、3D 深度学习组件等）能够迅速进入大众视野。此外，该清单特意排除了星标超过 4 万的超大型仓库，确保榜单更聚焦于成长型的新兴项目。\n\n这份清单非常适合 AI 开发者、算法研究员以及希望紧跟技术趋势的学生使用。无论是寻找最新的研究代码复现方案，还是探索 TensorFlow 2.0 或 PyTorch 的实战案例，用户都能在此发现灵感。其核心亮点在于利用 GitHub 搜索 API 精准锁定包含 CNN、RNN 等关键词的项目，并以直观的表格形式展示项目名称、简介、编程语言及具体的星标增长数据，为用户节省了大量检索与筛选时间，是把握深度学习脉搏的高效助手。","# Trending deep learning Github repositories\n\nHere's a list of top 100 deep learning Github trending repositories sorted by the number of stars gained on a specific day. 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\nRepositories with 40000 stars or more are excluded.\n\nTop deep learning Github repositories can be found [here](https:\u002F\u002Fgithub.com\u002Fmbadry1\u002FTop-Deep-Learning).\n\nDate: 02-02-2020 compared to 09-01-2019\n\nNote: This will be updated regularly.\n\n| | Pos1 | Name | Description | Language | Stars Today | Total Stars |\n|--------------------|------|---------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|-------------|-------------|\n| :new: | 1 | [spinningup](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fspinningup) | An educational resource to help anyone learn deep reinforcement learning. | Python | 53 | 4030 |\n| :arrow_up:2 | 2 | [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 | 18 | 15014 |\n| :new: | 3 | [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 | 17 | 6771 |\n| :arrow_up:15 | 4 | [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 | 16 | 10248 |\n| :arrow_up:1 | 5 | [DeepFaceLab](https:\u002F\u002Fgithub.com\u002Fiperov\u002FDeepFaceLab) | DeepFaceLab is the leading software for creating deep fakes. | Python | 15 | 12237 |\n| :new: | 6 | [pytorch3d](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fpytorch3d) | PyTorch3d is FAIR's library of reusable components for deep learning with 3D data. | Python | 15 | 544 |\n| :new: | 7 | [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 | 15 | 7092 |\n| :new: | 8 | [thinc](https:\u002F\u002Fgithub.com\u002Fexplosion\u002Fthinc) | 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries | Python | 15 | 1683 |\n| :arrow_up:15 | 9 | [pytorch-tutorial](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial) | PyTorch Tutorial for Deep Learning Researchers | Python | 14 | 15314 |\n| :arrow_up:39 | 10 | [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 | 14 | 5921 |\n| :arrow_up:3 | 11 | [pytorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch) | Tensors and Dynamic neural networks in Python with strong GPU acceleration | C++ | 14 | 35719 |\n| :arrow_up:35 | 12 | [pytorch_geometric](https:\u002F\u002Fgithub.com\u002Frusty1s\u002Fpytorch_geometric) | Geometric Deep Learning Extension Library for PyTorch | Python | 11 | 6473 |\n| :arrow_down:11 | 13 | [faceswap](https:\u002F\u002Fgithub.com\u002Fdeepfakes\u002Ffaceswap) | Deepfakes Software For All | Python | 11 | 28863 |\n| :new: | 14 | [streamlit](https:\u002F\u002Fgithub.com\u002Fstreamlit\u002Fstreamlit) | Streamlit — The fastest way to build custom ML tools | Python | 11 | 6650 |\n| :new: | 15 | [nni](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fnni) | An open source AutoML toolkit for neural architecture search, model compression and hyper-parameter tuning. | Python | 11 | 5281 |\n| :new: | 16 | [yolov3](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3) | YOLOv3 in PyTorch > ONNX > CoreML > iOS | Jupyter Notebook | 10 | 3400 |\n| :new: | 17 | [book](https:\u002F\u002Fgithub.com\u002FKeKe-Li\u002Fbook) | :books: All programming languages books | None | 10 | 4071 |\n| :arrow_up:28 | 18 | [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 | 10 | 10163 |\n| :arrow_up:13 | 19 | [TensorFlow-Examples](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples) | TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2) | Jupyter Notebook | 9 | 36173 |\n| :arrow_up:11 | 20 | [tfjs](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftfjs) | A WebGL accelerated JavaScript library for training and deploying ML models. | TypeScript | 9 | 12566 |\n| :new: | 21 | [carla](https:\u002F\u002Fgithub.com\u002Fcarla-simulator\u002Fcarla) | Open-source simulator for autonomous driving research. | C++ | 9 | 3885 |\n| :arrow_down:2 | 22 | [Mask_RCNN](https:\u002F\u002Fgithub.com\u002Fmatterport\u002FMask_RCNN) | Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow | Python | 9 | 15583 |\n| :new: | 23 | [jetson-inference](https:\u002F\u002Fgithub.com\u002Fdusty-nv\u002Fjetson-inference) | Guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson. | C++ | 9 | 2636 |\n| :arrow_up:35 | 24 | [awesome-deep-learning](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning) | A curated list of awesome Deep Learning tutorials, projects and communities. | None | 8 | 14565 |\n| :new: | 25 | [catalyst](https:\u002F\u002Fgithub.com\u002Fcatalyst-team\u002Fcatalyst) | Accelerated DL & RL | Python | 8 | 1544 |\n| :arrow_up:62 | 26 | [awesome-project-ideas](https:\u002F\u002Fgithub.com\u002FNirantK\u002Fawesome-project-ideas) | Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas | None | 8 | 3381 |\n| :arrow_down:2 | 27 | [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 | 8 | 15910 |\n| :arrow_down:12 | 28 | [pandas-profiling](https:\u002F\u002Fgithub.com\u002Fpandas-profiling\u002Fpandas-profiling) | Create HTML profiling reports from pandas DataFrame objects | Python | 8 | 4290 |\n| :arrow_down:17 | 29 | [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 | 8 | 22923 |\n| :new: | 30 | [spleeter](https:\u002F\u002Fgithub.com\u002Fdeezer\u002Fspleeter) | Deezer source separation library including pretrained models. | Python | 7 | 9752 |\n| :new: | 31 | [pytorch-lightning](https:\u002F\u002Fgithub.com\u002FPyTorchLightning\u002Fpytorch-lightning) | The lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate | Python | 7 | 3512 |\n| :new: | 32 | [photoprism](https:\u002F\u002Fgithub.com\u002Fphotoprism\u002Fphotoprism) | Personal Photo Management powered by Go and Google TensorFlow | Go | 7 | 4623 |\n| :arrow_down:22 | 33 | [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 | 7 | 18622 |\n| :new: | 34 | [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 | 7 | 25457 |\n| :heavy_minus_sign: | 35 | [deeplearning-models](https:\u002F\u002Fgithub.com\u002Frasbt\u002Fdeeplearning-models) | A collection of various deep learning architectures, models, and tips | Jupyter Notebook | 7 | 11482 |\n| :new: | 36 | [trax](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Ftrax) | Trax — your path to advanced deep learning | Jupyter Notebook | 7 | 1649 |\n| :new: | 37 | [practicalAI](https:\u002F\u002Fgithub.com\u002FpracticalAI\u002FpracticalAI) | 📚 A practical approach to machine learning. | Jupyter Notebook | 7 | 23437 |\n| :new: | 38 | [fashion-mnist](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffashion-mnist) | A MNIST-like fashion product database. Benchmark :point_right: | Python | 6 | 7160 |\n| :arrow_up:61 | 39 | [mit-deep-learning](https:\u002F\u002Fgithub.com\u002Flexfridman\u002Fmit-deep-learning) | Tutorials, assignments, and competitions for MIT Deep Learning related courses. | Jupyter Notebook | 6 | 6899 |\n| :new: | 40 | [label-studio](https:\u002F\u002Fgithub.com\u002Fheartexlabs\u002Flabel-studio) | Label Studio is a multi-type data labeling and annotation tool with standardized output format | JavaScript | 6 | 2379 |\n| :new: | 41 | [ASRT_SpeechRecognition](https:\u002F\u002Fgithub.com\u002Fnl8590687\u002FASRT_SpeechRecognition) | A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统 | Python | 6 | 2437 |\n| :new: | 42 | [nlp_overview](https:\u002F\u002Fgithub.com\u002Fomarsar\u002Fnlp_overview) | Overview of Modern Deep Learning Techniques Applied to Natural Language Processing | CSS | 6 | 844 |\n| :arrow_up:8 | 43 | [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 | 6 | 23326 |\n| :new: | 44 | [spaCy](https:\u002F\u002Fgithub.com\u002Fexplosion\u002FspaCy) | 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython | Python | 6 | 15643 |\n| :arrow_up:42 | 45 | [facenet](https:\u002F\u002Fgithub.com\u002Fdavidsandberg\u002Ffacenet) | Face recognition using Tensorflow | Python | 6 | 9965 |\n| :arrow_up:33 | 46 | [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 | 6 | 9888 |\n| :arrow_up:7 | 47 | [dlaicourse](https:\u002F\u002Fgithub.com\u002Flmoroney\u002Fdlaicourse) | Notebooks for learning deep learning | Jupyter Notebook | 5 | 2184 |\n| :arrow_up:16 | 48 | [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 | 5 | 6350 |\n| :arrow_up:9 | 49 | [autokeras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fautokeras) | An AutoML system based on Keras | Python | 5 | 6561 |\n| :new: | 50 | [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 | 5 | 3910 |\n| :new: | 51 | [Keras-GAN](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FKeras-GAN) | Keras implementations of Generative Adversarial Networks. | Python | 5 | 6450 |\n| :arrow_up:19 | 52 | [dgl](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fdgl) | Python package built to ease deep learning on graph, on top of existing DL frameworks. | Python | 5 | 3944 |\n| :arrow_down:17 | 53 | [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 | 5 | 4348 |\n| :new: | 54 | [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 | 5 | 5239 |\n| :arrow_up:38 | 55 | [deep-learning-coursera](https:\u002F\u002Fgithub.com\u002FKulbear\u002Fdeep-learning-coursera) | Deep Learning Specialization by Andrew Ng on Coursera. | Jupyter Notebook | 5 | 4773 |\n| :arrow_up:30 | 56 | [nlp-tutorial](https:\u002F\u002Fgithub.com\u002Fgraykode\u002Fnlp-tutorial) | Natural Language Processing Tutorial for Deep Learning Researchers | Jupyter Notebook | 5 | 5176 |\n| :new: | 57 | [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 | 5 | 9350 |\n| :new: | 58 | [PySyft](https:\u002F\u002Fgithub.com\u002FOpenMined\u002FPySyft) | A library for encrypted, privacy preserving machine learning | Python | 5 | 4819 |\n| :new: | 59 | [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 | 5 | 9953 |\n| :arrow_up:21 | 60 | [deeplearningbook-chinese](https:\u002F\u002Fgithub.com\u002Fexacity\u002Fdeeplearningbook-chinese) | Deep Learning Book Chinese Translation | TeX | 5 | 27753 |\n| :arrow_down:51 | 61 | [fastai](https:\u002F\u002Fgithub.com\u002Ffastai\u002Ffastai) | The fastai deep learning library, plus lessons and tutorials | Jupyter Notebook | 5 | 17001 |\n| :new: | 62 | [DeepSpeech](https:\u002F\u002Fgithub.com\u002Fmozilla\u002FDeepSpeech) | A TensorFlow implementation of Baidu's DeepSpeech architecture | C++ | 5 | 12951 |\n| :arrow_down:15 | 63 | [darknet](https:\u002F\u002Fgithub.com\u002Fpjreddie\u002Fdarknet) | Convolutional Neural Networks | C | 5 | 16203 |\n| :arrow_down:23 | 64 | [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++ | 5 | 15825 |\n| :new: | 65 | [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++ | 4 | 3914 |\n| :new: | 66 | [Dive-into-DL-TensorFlow2.0](https:\u002F\u002Fgithub.com\u002FTrickyGo\u002FDive-into-DL-TensorFlow2.0) | 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意 | Jupyter Notebook | 4 | 1773 |\n| :new: | 67 | [Practical_RL](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002FPractical_RL) | A course in reinforcement learning in the wild | Jupyter Notebook | 4 | 3716 |\n| :arrow_up:30 | 68 | [Awesome-PyTorch-Chinese](https:\u002F\u002Fgithub.com\u002FINTERMT\u002FAwesome-PyTorch-Chinese) | 【干货】史上最全的PyTorch学习资源汇总 | Python | 4 | 1932 |\n| :new: | 69 | [ICCV2019-LearningToPaint](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FICCV2019-LearningToPaint) | ICCV2019 - A painting AI that can reproduce paintings stroke by stroke using deep reinforcement learning. | Python | 4 | 1583 |\n| :arrow_down:5 | 70 | [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 | 4 | 3790 |\n| :arrow_down:4 | 71 | [Stock-Prediction-Models](https:\u002F\u002Fgithub.com\u002Fhuseinzol05\u002FStock-Prediction-Models) | Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations | Jupyter Notebook | 4 | 1408 |\n| :new: | 72 | [deeplearning4j](https:\u002F\u002Fgithub.com\u002Feclipse\u002Fdeeplearning4j) | Eclipse Deeplearning4j, ND4J, DataVec and more - deep learning & linear algebra for Java\u002FScala with GPUs + Spark | Java | 4 | 11454 |\n| :arrow_down:16 | 73 | [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 | 4 | 6681 |\n| :new: | 74 | [Deep-learning-books](https:\u002F\u002Fgithub.com\u002Floveunk\u002FDeep-learning-books) | Books for machine learning, deep learning, math, NLP, CV, RL, etc. 一些机器学习、深度学习等相关话题的书籍。 | None | 4 | 730 |\n| :arrow_down:1 | 75 | [labelImg](https:\u002F\u002Fgithub.com\u002Ftzutalin\u002FlabelImg) | 🖍️ LabelImg is a graphical image annotation tool and label object bounding boxes in images | Python | 4 | 9635 |\n| :new: | 76 | [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 | 4 | 4017 |\n| :arrow_up:8 | 77 | [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 | 4 | 7534 |\n| :arrow_up:16 | 78 | [machine_learning_examples](https:\u002F\u002Fgithub.com\u002Flazyprogrammer\u002Fmachine_learning_examples) | A collection of machine learning examples and tutorials. | Python | 4 | 4232 |\n| :new: | 79 | [albumentations](https:\u002F\u002Fgithub.com\u002Falbumentations-team\u002Falbumentations) | fast image augmentation library and easy to use wrapper around other libraries | Python | 4 | 4336 |\n| :arrow_down:73 | 80 | [mediapipe](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fmediapipe) | MediaPipe is a cross-platform framework for building multimodal applied machine learning pipelines | C++ | 4 | 4458 |\n| :new: | 81 | [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 | 4 | 4463 |\n| :new: | 82 | [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 | 4 | 17947 |\n| :arrow_down:5 | 83 | [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 | 4 | 18344 |\n| :new: | 84 | [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 | 4 | 4703 |\n| :arrow_down:32 | 85 | [caffe](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe) | Caffe: a fast open framework for deep learning. | C++ | 4 | 29775 |\n| :new: | 86 | [wav2letter](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fwav2letter) | Facebook AI Research's Automatic Speech Recognition Toolkit | C++ | 4 | 4806 |\n| :new: | 87 | [seq2seq-couplet](https:\u002F\u002Fgithub.com\u002Fwb14123\u002Fseq2seq-couplet) | Play couplet with seq2seq model. 用深度学习对对联。 | Python | 4 | 4060 |\n| :new: | 88 | [open_model_zoo](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fopen_model_zoo) | Pre-trained Deep Learning models and samples (high quality and extremely fast) | Python | 4 | 1709 |\n| :new: | 89 | [ml-agents](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents) | Unity Machine Learning Agents Toolkit | Python | 4 | 7685 |\n| :arrow_down:10 | 90 | [DeepLearningExamples](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples) | Deep Learning Examples | Jupyter Notebook | 4 | 3060 |\n| :new: | 91 | [deep-high-resolution-net.pytorch](https:\u002F\u002Fgithub.com\u002Fleoxiaobin\u002Fdeep-high-resolution-net.pytorch) | The project is an official implementation of our CVPR2019 paper \"Deep High-Resolution Representation Learning for Human Pose Estimation\" | Cuda | 4 | 2177 |\n| :new: | 92 | [awesome-mlss](https:\u002F\u002Fgithub.com\u002Fsshkhr\u002Fawesome-mlss) | List of summer schools in machine learning + related fields across the globe | None | 4 | 621 |\n| :arrow_down:23 | 93 | [DeepLearning](https:\u002F\u002Fgithub.com\u002FMikoto10032\u002FDeepLearning) | 深度学习入门教程&&优秀文章&&Deep Learning Tutorial | Jupyter Notebook | 4 | 2308 |\n| :arrow_up:1 | 94 | [horovod](https:\u002F\u002Fgithub.com\u002Fhorovod\u002Fhorovod) | Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. | Python | 4 | 8517 |\n| :new: | 95 | [deep-learning-v2-pytorch](https:\u002F\u002Fgithub.com\u002Fudacity\u002Fdeep-learning-v2-pytorch) | Projects and exercises for the latest Deep Learning ND program https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fdeep-learning-nanodegree--nd101 | Jupyter Notebook | 4 | 2733 |\n| :new: | 96 | [cortex](https:\u002F\u002Fgithub.com\u002Fcortexlabs\u002Fcortex) | Deploy machine learning models in production | Go | 4 | 2848 |\n| :new: | 97 | [pytorch-summary](https:\u002F\u002Fgithub.com\u002Fsksq96\u002Fpytorch-summary) | Model summary in PyTorch similar to `model.summary()` in Keras | Python | 4 | 1952 |\n| :new: | 98 | [pwnagotchi](https:\u002F\u002Fgithub.com\u002Fevilsocket\u002Fpwnagotchi) | (⌐■_■) - Deep Reinforcement Learning instrumenting bettercap for WiFi pwning. | JavaScript | 4 | 3162 |\n| :new: | 99 | [cvat](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fcvat) | Powerful and efficient Computer Vision Annotation Tool (CVAT) | Python | 3 | 3074 |\n| :arrow_down:56 | 100 | [AI-Job-Notes](https:\u002F\u002Fgithub.com\u002Famusi\u002FAI-Job-Notes) | AI算法岗求职攻略（涵盖准备攻略、刷题指南、内推和AI公司清单等资料） | None | 3 | 1857 |","# 深度学习领域的 GitHub 趋势仓库\n\n以下是按特定日期新增星数排序的前 100 个深度学习 GitHub 趋势仓库列表。用于 GitHub 搜索 API 的查询语句如下：\n\n- `deep-learning OR CNN OR RNN OR \"convolutional neural network\" OR \"recurrent neural network\"`\n\n已排除星数达到或超过 40000 的仓库。\n\n深度学习领域的顶级 GitHub 仓库可以在此处找到：[https:\u002F\u002Fgithub.com\u002Fmbadry1\u002FTop-Deep-Learning](https:\u002F\u002Fgithub.com\u002Fmbadry1\u002FTop-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| | 排名1 | 名称 | 描述 | 语言 | 今日星数 | 总星数 |\n|--------------------|------|---------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|-------------|-------------|\n| :new: | 1 | [spinningup](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fspinningup) | 一个帮助任何人学习深度强化学习的教育资源。 | Python | 53 | 4030 |\n| :arrow_up:2 | 2 | [Real-Time-Voice-Cloning](https:\u002F\u002Fgithub.com\u002FCorentinJ\u002FReal-Time-Voice-Cloning) | 在5秒内克隆声音，实现实时任意语音生成 | Python | 18 | 15014 |\n| :new: | 3 | [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 | 17 | 6771 |\n| :arrow_up:15 | 4 | [ray](https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray) | 一个用于构建和运行分布式应用的快速且简单的框架。Ray内置了可扩展的强化学习库RLlib和超参数调优库Tune。 | Python | 16 | 10248 |\n| :arrow_up:1 | 5 | [DeepFaceLab](https:\u002F\u002Fgithub.com\u002Fiperov\u002FDeepFaceLab) | DeepFaceLab是制作深度伪造内容的领先软件。 | Python | 15 | 12237 |\n| :new: | 6 | [pytorch3d](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fpytorch3d) | PyTorch3d是FAIR为处理3D数据的深度学习提供的可重用组件库。 | Python | 15 | 544 |\n| :new: | 7 | [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 | 15 | 7092 |\n| :new: | 8 | [thinc](https:\u002F\u002Fgithub.com\u002Fexplosion\u002Fthinc) | 🔮 一种令人耳目一新的函数式深度学习方法，兼容你喜爱的各类库 | Python | 15 | 1683 |\n| :arrow_up:15 | 9 | [pytorch-tutorial](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fpytorch-tutorial) | 面向深度学习研究者的PyTorch教程 | Python | 14 | 15314 |\n| :arrow_up:39 | 10 | [handson-ml2](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml2) | 一系列Jupyter笔记本，通过使用Scikit-Learn、Keras和TensorFlow 2，带你逐步掌握机器学习和深度学习的基础知识。 | Jupyter Notebook | 14 | 5921 |\n| :arrow_up:3 | 11 | [pytorch](https:\u002F\u002Fgithub.com\u002Fpytorch\u002Fpytorch) | 在Python中使用张量和动态神经网络，并具备强大的GPU加速功能 | C++ | 14 | 35719 |\n| :arrow_up:35 | 12 | [pytorch_geometric](https:\u002F\u002Fgithub.com\u002Frusty1s\u002Fpytorch_geometric) | 面向PyTorch的几何深度学习扩展库 | Python | 11 | 6473 |\n| :arrow_down:11 | 13 | [faceswap](https:\u002F\u002Fgithub.com\u002Fdeepfakes\u002Ffaceswap) | 适用于所有人的深度伪造软件 | Python | 11 | 28863 |\n| :new: | 14 | [streamlit](https:\u002F\u002Fgithub.com\u002Fstreamlit\u002Fstreamlit) | Streamlit — 构建自定义ML工具的最快方式 | Python | 11 | 6650 |\n| :new: | 15 | [nni](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fnni) | 一个用于神经架构搜索、模型压缩和超参数调优的开源AutoML工具包。 | Python | 11 | 5281 |\n| :new: | 16 | [yolov3](https:\u002F\u002Fgithub.com\u002Fultralytics\u002Fyolov3) | YOLOv3在PyTorch > ONNX > CoreML > iOS中的实现 | Jupyter Notebook | 10 | 3400 |\n| :new: | 17 | [book](https:\u002F\u002Fgithub.com\u002FKeKe-Li\u002Fbook) | :books: 所有编程语言书籍 | 无 | 10 | 4071 |\n| :arrow_up:28 | 18 | [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 | 10 | 10163 |\n| :arrow_up:13 | 19 | [TensorFlow-Examples](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples) | 面向初学者的TensorFlow教程和示例（支持TF v1和v2） | Jupyter Notebook | 9 | 36173 |\n| :arrow_up:11 | 20 | [tfjs](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftfjs) | 一个基于WebGL加速的JavaScript库，用于训练和部署机器学习模型。 | TypeScript | 9 | 12566 |\n| :new: | 21 | [carla](https:\u002F\u002Fgithub.com\u002Fcarla-simulator\u002Fcarla) | 用于自动驾驶研究的开源模拟器。 | C++ | 9 | 3885 |\n| :arrow_down:2 | 22 | [Mask_RCNN](https:\u002F\u002Fgithub.com\u002Fmatterport\u002FMask_RCNN) | 基于Keras和TensorFlow的目标检测与实例分割模型Mask R-CNN | Python | 9 | 15583 |\n| :new: | 23 | [jetson-inference](https:\u002F\u002Fgithub.com\u002Fdusty-nv\u002Fjetson-inference) | 使用TensorRT和NVIDIA Jetson部署深度学习推理网络及深度视觉原语的指南。 | C++ | 9 | 2636 |\n| :arrow_up:35 | 24 | [awesome-deep-learning](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning) | 精选的深度学习教程、项目和社区列表。 | 无 | 8 | 14565 |\n| :new: | 25 | [catalyst](https:\u002F\u002Fgithub.com\u002Fcatalyst-team\u002Fcatalyst) | 加速的DL & RL | Python | 8 | 1544 |\n| :arrow_up:62 | 26 | [awesome-project-ideas](https:\u002F\u002Fgithub.com\u002FNirantK\u002Fawesome-project-ideas) | 精选的机器学习、NLP、视觉、推荐系统项目创意列表 | 无 | 8 | 3381 |\n| :arrow_down:2 | 27 | [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 | 8 | 15910 |\n| :arrow_down:12 | 28 | [pandas-profiling](https:\u002F\u002Fgithub.com\u002Fpandas-profiling\u002Fpandas-profiling) | 从pandas DataFrame对象中创建HTML分析报告 | Python | 8 | 4290 |\n| :arrow_down:17 | 29 | [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 | 8 | 22923 |\n| :new: | 30 | [spleeter](https:\u002F\u002Fgithub.com\u002Fdeezer\u002Fspleeter) | 包括预训练模型的Deezer音源分离库。 | Python | 7 | 9752 |\n| :new: | 31 | [pytorch-lightning](https:\u002F\u002Fgithub.com\u002FPyTorchLightning\u002Fpytorch-lightning) | 面向ML研究人员的轻量级PyTorch封装。扩展你的模型，减少样板代码。 | Python | 7 | 3512 |\n| :new: | 32 | [photoprism](https:\u002F\u002Fgithub.com\u002Fphotoprism\u002Fphotoprism) | 由Go和Google TensorFlow驱动的个人照片管理工具 | Go | 7 | 4623 |\n| :arrow_down:22 | 33 | [handson-ml](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml) | 一系列Jupyter笔记本，通过使用Scikit-Learn和TensorFlow，带你逐步掌握机器学习和深度学习的基础知识。 | Jupyter Notebook | 7 | 18622 |\n| :new: | 34 | [Deep-Learning-Papers-Reading-Roadmap](https:\u002F\u002Fgithub.com\u002Ffloodsung\u002FDeep-Learning-Papers-Reading-Roadmap) | 任何渴望学习这项惊人技术的人的深度学习论文阅读路线图！ | Python | 7 | 25457 |\n| :heavy_minus_sign: | 35 | [deeplearning-models](https:\u002F\u002Fgithub.com\u002Frasbt\u002Fdeeplearning-models) | 各种深度学习架构、模型和技巧的集合 | Jupyter Notebook | 7 | 11482 |\n| :new: | 36 | [trax](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Ftrax) | Trax — 通往高级深度学习的道路 | Jupyter Notebook | 7 | 1649 |\n| :new: | 37 | [practicalAI](https:\u002F\u002Fgithub.com\u002FpracticalAI\u002FpracticalAI) | 📚 一种实用的机器学习方法。 | Jupyter Notebook | 7 | 23437 |\n| :new: | 38 | [fashion-mnist](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffashion-mnist) | 类似MNIST的时尚产品数据库。基准测试 :point_right: | Python | 6 | 7160 |\n| :arrow_up:61 | 39 | [mit-deep-learning](https:\u002F\u002Fgithub.com\u002Flexfridman\u002Fmit-deep-learning) | 麻省理工学院深度学习相关课程的教程、作业和竞赛。 | Jupyter Notebook | 6 | 6899 |\n| :new: | 40 | [label-studio](https:\u002F\u002Fgithub.com\u002Fheartexlabs\u002Flabel-studio) | Label Studio是一款多类型数据标注和注释工具，具有标准化的输出格式 | JavaScript | 6 | 2379 |\n| :new: | 41 | [ASRT_SpeechRecognition](https:\u002F\u002Fgithub.com\u002Fnl8590687\u002FASRT_SpeechRecognition) | A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统 | Python | 6 | 2437 |\n| :new: | 42 | [nlp_overview](https:\u002F\u002Fgithub.com\u002Fomarsar\u002Fnlp_overview) | 现代深度学习技术在自然语言处理中的概述 | CSS | 6 | 844 |\n| :arrow_up:8 | 43 | [machine-learning-for-software-engineers](https:\u002F\u002Fgithub.com\u002FZuzooVn\u002Fmachine-learning-for-software-engineers) | 成为机器学习工程师的学习完整每日计划。 | 无 | 6 | 23326 |\n| :new: | 44 | [spaCy](https:\u002F\u002Fgithub.com\u002Fexplosion\u002FspaCy) | 💫 强大的工业级自然语言处理（NLP）工具，结合Python和Cython | Python | 6 | 15643 |\n| :arrow_up:42 | 45 | [facenet](https:\u002F\u002Fgithub.com\u002Fdavidsandberg\u002Ffacenet) | 使用Tensorflow进行人脸识别 | Python | 6 | 9965 |\n| :arrow_up:33 | 46 | [stanford-cs-229-machine-learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning) | 斯坦福CS 229机器学习课程的VIP备忘录 | 无 | 6 | 9888 |\n| :arrow_up:7 | 47 | [dlaicourse](https:\u002F\u002Fgithub.com\u002Flmoroney\u002Fdlaicourse) | 学习深度学习的笔记本 | Jupyter Notebook | 5 | 2184 |\n| :arrow_up:16 | 48 | [ludwig](https:\u002F\u002Fgithub.com\u002Fuber\u002Fludwig) | Ludwig是一个基于TensorFlow构建的工具箱，允许在无需编写代码的情况下训练和测试深度学习模型。 | Python | 5 | 6350 |\n| :arrow_up:9 | 49 | [autokeras](https:\u002F\u002Fgithub.com\u002Fkeras-team\u002Fautokeras) | 一个基于Keras的AutoML系统 | Python | 5 | 6561 |\n| :new: | 50 | [models](https:\u002F\u002Fgithub.com\u002FPaddlePaddle\u002Fmodels) | 预训练和复现的深度学习模型（『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 | 5 | 3910 |\n| :new: | 51 | [Keras-GAN](https:\u002F\u002Fgithub.com\u002Feriklindernoren\u002FKeras-GAN) | 生成对抗网络的Keras实现。 | Python | 5 | 6450 |\n| :arrow_up:19 | 52 | [dgl](https:\u002F\u002Fgithub.com\u002Fdmlc\u002Fdgl) | 一个基于现有深度学习框架构建的Python包，旨在简化图上的深度学习。 | Python | 5 | 3944 |\n| :arrow_down:17 | 53 | [TensorFlow-2.x-Tutorials](https:\u002F\u002Fgithub.com\u002Fdragen1860\u002FTensorFlow-2.x-Tutorials) | 包含CNN、RNN、GAN、自动编码器、FasterRCNN、GPT、BERT等示例的TensorFlow 2.0版本入门实例代码，实战教程。 | Jupyter Notebook | 5 | 4348 |\n| :new: | 54 | [awesome-artificial-intelligence](https:\u002F\u002Fgithub.com\u002Fowainlewis\u002Fawesome-artificial-intelligence) | 精选的人工智能（AI）课程、书籍、视频讲座和论文列表 | 无 | 5 | 5239 |\n| :arrow_up:38 | 55 | [deep-learning-coursera](https:\u002F\u002Fgithub.com\u002FKulbear\u002Fdeep-learning-coursera) | Andrew Ng在Coursera上的深度学习专项课程。 | Jupyter Notebook | 5 | 4773 |\n| :arrow_up:30 | 56 | [nlp-tutorial](https:\u002F\u002Fgithub.com\u002Fgraykode\u002Fnlp-tutorial) | 面向深度学习研究者的自然语言处理教程 | Jupyter Notebook | 5 | 5176 |\n| :new: | 57 | [deep-learning-with-python-notebooks](https:\u002F\u002Fgithub.com\u002Ffchollet\u002Fdeep-learning-with-python-notebooks) | 《Python深度学习》一书中的代码示例Jupyter笔记本 | Jupyter Notebook | 5 | 9350 |\n| :new: | 58 | [PySyft](https:\u002F\u002Fgithub.com\u002FOpenMined\u002FPySyft) | 一个用于加密、保护隐私的机器学习库 | Python | 5 | 4819 |\n| :new: | 59 | [char-rnn](https:\u002F\u002Fgithub.com\u002Fkarpathy\u002Fchar-rnn) | 多层循环神经网络（LSTM、GRU、RNN）用于字符级别的语言模型，在Torch中实现 | Lua | 5 | 9953 |\n| :arrow_up:21 | 60 | [deeplearningbook-chinese](https:\u002F\u002Fgithub.com\u002Fexacity\u002Fdeeplearningbook-chinese) | 《深度学习》中文译本 | TeX | 5 | 27753 |\n| :arrow_down:51 | 61 | [fastai](https:\u002F\u002Fgithub.com\u002Ffastai\u002Ffastai) | fastai深度学习库，附带课程和教程 | Jupyter Notebook | 5 | 17001 |\n| :new: | 62 | [DeepSpeech](https:\u002F\u002Fgithub.com\u002Fmozilla\u002FDeepSpeech) | 百度DeepSpeech架构的TensorFlow实现 | C++ | 5 | 12951 |\n| :arrow_down:15 | 63 | [darknet](https:\u002F\u002Fgithub.com\u002Fpjreddie\u002Fdarknet) | 卷积神经网络 | C | 5 | 16203 |\n| :arrow_down:23 | 64 | [openpose](https:\u002F\u002Fgithub.com\u002FCMU-Perceptual-Computing-Lab\u002Fopenpose) | OpenPose：实时多人关键点检测库，可用于人体、面部、手部和脚部的姿态估计 | C++ | 5 | 15825 |\n| :new: | 65 | [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++ | 4 | 3914 |\n| :new: | 66 | [Dive-into-DL-TensorFlow2.0](https:\u002F\u002Fgithub.com\u002FTrickyGo\u002FDive-into-DL-TensorFlow2.0) | 本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意 | Jupyter Notebook | 4 | 1773 |\n| :new: | 67 | [Practical_RL](https:\u002F\u002Fgithub.com\u002Fyandexdataschool\u002FPractical_RL) | 一场野外的强化学习课程 | Jupyter Notebook | 4 | 3716 |\n| :arrow_up:30 | 68 | [Awesome-PyTorch-Chinese](https:\u002F\u002Fgithub.com\u002FINTERMT\u002FAwesome-PyTorch-Chinese) | 【干货】史上最全的PyTorch学习资源汇总 | Python | 4 | 1932 |\n| :new: | 69 | [ICCV2019-LearningToPaint](https:\u002F\u002Fgithub.com\u002Fhzwer\u002FICCV2019-LearningToPaint) | ICCV2019 - 一款能够通过深度强化学习逐笔还原绘画作品的AI绘画程序。 | Python | 4 | 1583 |\n| :arrow_down:5 | 70 | [d2l-en](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-en) | Dive into Deep Learning：一本基于NumPy接口的交互式深度学习书籍，包含代码、数学和讨论。 | Python | 4 | 3790 |\n| :arrow_down:4 | 71 | [Stock-Prediction-Models](https:\u002F\u002Fgithub.com\u002Fhuseinzol05\u002FStock-Prediction-Models) | 收集了用于股票预测的机器学习和深度学习模型，包括交易机器人和模拟程序 | Jupyter Notebook | 4 | 1408 |\n| :new: | 72 | [deeplearning4j](https:\u002F\u002Fgithub.com\u002Feclipse\u002Fdeeplearning4j) | Eclipse Deeplearning4j、ND4J、DataVec等——面向Java\u002FScala的深度学习和线性代数，支持GPU + Spark | Java | 4 | 11454 |\n| :arrow_down:16 | 73 | [bert-as-service](https:\u002F\u002Fgithub.com\u002Fhanxiao\u002Fbert-as-service) | 使用BERT模型将变长句子映射为固定长度向量 | Python | 4 | 6681 |\n| :new: | 74 | [Deep-learning-books](https:\u002F\u002Fgithub.com\u002Floveunk\u002FDeep-learning-books) | 一些机器学习、深度学习等相关话题的书籍。 | 无 | 4 | 730 |\n| :arrow_down:1 | 75 | [labelImg](https:\u002F\u002Fgithub.com\u002Ftzutalin\u002FlabelImg) | 🖍️ LabelImg是一款图形化的图像标注工具，用于在图片中绘制标签框 | Python | 4 | 9635 |\n| :new: | 76 | [stanford-cs-230-deep-learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-230-deep-learning) | 斯坦福CS 230深度学习课程的VIP备忘录 | 无 | 4 | 4017 |\n| :arrow_up:8 | 77 | [mit-deep-learning-book-pdf](https:\u002F\u002Fgithub.com\u002Fjanishar\u002Fmit-deep-learning-book-pdf) | 伊恩·古德费洛、约书亚·本吉奥和阿伦·库维尔合著的MIT深度学习书籍的PDF版本（完整版和部分章节） | Java | 4 | 7534 |\n| :arrow_up:16 | 78 | [machine_learning_examples](https:\u002F\u002Fgithub.com\u002Flazyprogrammer\u002Fmachine_learning_examples) | 一系列机器学习示例和教程。 | Python | 4 | 4232 |\n| :new: | 79 | [albumentations](https:\u002F\u002Fgithub.com\u002Falbumentations-team\u002Falbumentations) | 快速图像增强库，也是其他库的易用封装 | Python | 4 | 4336 |\n| :arrow_down:73 | 80 | [mediapipe](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fmediapipe) | MediaPipe是一个跨平台框架，用于构建多模态应用机器学习管道 | C++ | 4 | 4458 |\n| :new: | 81 | [the-incredible-pytorch](https:\u002F\u002Fgithub.com\u002Fritchieng\u002Fthe-incredible-pytorch) | The Incredible PyTorch：一份精选的PyTorch相关教程、论文、项目、社区等资源列表。 | 无 | 4 | 4463 |\n| :new: | 82 | [data-science-ipython-notebooks](https:\u002F\u002Fgithub.com\u002Fdonnemartin\u002Fdata-science-ipython-notebooks) | 数据科学Python笔记本：深度学习（TensorFlow、Theano、Caffe、Keras）、scikit-learn、Kaggle、大数据（Spark、Hadoop MapReduce、HDFS）、matplotlib、pandas、NumPy、SciPy、Python基础、AWS以及各种命令行。 | Python | 4 | 17947 |\n| :arrow_down:5 | 83 | [incubator-mxnet](https:\u002F\u002Fgithub.com\u002Fapache\u002Fincubator-mxnet) | 轻量级、便携、灵活的分布式\u002F移动深度学习，采用动态、支持突变的数据流依赖调度；适用于Python、R、Julia、Scala、Go、JavaScript等多种语言 | Python | 4 | 18344 |\n| :new: | 84 | [face_classification](https:\u002F\u002Fgithub.com\u002Foarriaga\u002Fface_classification) | 实时人脸检测及情绪\u002F性别分类，使用fer2013\u002Fimdb数据集，配合Keras CNN模型和OpenCV。 | Python | 4 | 4703 |\n| :arrow_down:32 | 85 | [caffe](https:\u002F\u002Fgithub.com\u002FBVLC\u002Fcaffe) | Caffe：一个快速的开源深度学习框架。 | C++ | 4 | 29775 |\n| :new: | 86 | [wav2letter](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fwav2letter) | Facebook AI Research的自动语音识别工具包 | C++ | 4 | 4806 |\n| :new: | 87 | [seq2seq-couplet](https:\u002F\u002Fgithub.com\u002Fwb14123\u002Fseq2seq-couplet) | 用深度学习对对联。 | Python | 4 | 4060 |\n| :new: | 88 | [open_model_zoo](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fopen_model_zoo) | 预训练的深度学习模型和样本（高质量且极快） | Python | 4 | 1709 |\n| :new: | 89 | [ml-agents](https:\u002F\u002Fgithub.com\u002FUnity-Technologies\u002Fml-agents) | Unity机器学习代理工具包 | Python | 4 | 7685 |\n| :arrow_down:10 | 90 | [DeepLearningExamples](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FDeepLearningExamples) | 需要深度学习的例子 | Jupyter Notebook | 4 | 3060 |\n| :new: | 91 | [deep-high-resolution-net.pytorch](https:\u002F\u002Fgithub.com\u002Fleoxiaobin\u002Fdeep-high-resolution-net.pytorch) | 该项目是我们CVPR2019论文“用于人体姿态估计的深度高分辨率表征学习”的官方实现 | Cuda | 4 | 2177 |\n| :new: | 92 | [awesome-mlss](https:\u002F\u002Fgithub.com\u002Fsshkhr\u002Fawesome-mlss) | 全球范围内机器学习及相关领域的暑期学校列表 | 无 | 4 | 621 |\n| :arrow_down:23 | 93 | [DeepLearning](https:\u002F\u002Fgithub.com\u002FMikoto10032\u002FDeepLearning) | 深度学习入门教程&&优秀文章&&Deep Learning Tutorial | Jupyter Notebook | 4 | 2308 |\n| :arrow_up:1 | 94 | [horovod](https:\u002F\u002Fgithub.com\u002Fhorovod\u002Fhorovod) | 面向TensorFlow、Keras、PyTorch和Apache MXNet的分布式训练框架。 | Python | 4 | 8517 |\n| :new: | 95 | [deep-learning-v2-pytorch](https:\u002F\u002Fgithub.com\u002Fudacity\u002Fdeep-learning-v2-pytorch) | 最新深度学习ND项目的项目和练习 https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fdeep-learning-nanodegree--nd101 | Jupyter Notebook | 4 | 2733 |\n| :new: | 96 | [cortex](https:\u002F\u002Fgithub.com\u002Fcortexlabs\u002Fcortex) | 将机器学习模型部署到生产环境中 | Go | 4 | 2848 |\n| :new: | 97 | [pytorch-summary](https:\u002F\u002Fgithub.com\u002Fsksq96\u002Fpytorch-summary) | PyTorch中的模型摘要，类似于Keras中的`model.summary()` | Python | 4 | 1952 |\n| :new: | 98 | [pwnagotchi](https:\u002F\u002Fgithub.com\u002Fevilsocket\u002Fpwnagotchi) | (⌐■_■) - 深度强化学习，利用bettercap进行WiFi密码破解。 | JavaScript | 4 | 3162 |\n| :new: | 99 | [cvat](https:\u002F\u002Fgithub.com\u002Fopencv\u002Fcvat) | 功能强大且高效的计算机视觉标注工具（CVAT） | Python | 3 | 3074 |\n| :arrow_down:56 | 100 | [AI-Job-Notes](https:\u002F\u002Fgithub.com\u002Famusi\u002FAI-Job-Notes) | AI算法岗求职攻略（涵盖准备攻略、刷题指南、内推和AI公司清单等资料） | 无 | 3 | 1857 |","# Trending-Deep-Learning 快速上手指南\n\n**工具简介**：\nTrending-Deep-Learning 并非一个可安装的软件库，而是一个动态更新的 GitHub 仓库列表。它整理了每日获得 Star 数最多的前 100 个深度学习开源项目（排除已超 4 万 Star 的成熟项目），涵盖强化学习、语音克隆、计算机视觉、自动机器学习（AutoML）等前沿领域。本指南旨在帮助开发者如何利用该列表发现并快速启动感兴趣的热门项目。\n\n## 环境准备\n\n由于列表中包含多种类型的深度学习项目（如 PyTorch, TensorFlow, Ray 等），建议准备通用的深度学习开发环境。\n\n* **操作系统**：Linux (Ubuntu 18.04+ 推荐), macOS, 或 Windows (WSL2)\n* **Python 版本**：Python 3.7 - 3.9 (大多数深度学习项目对此范围支持最好)\n* **前置依赖**：\n * Git\n * CUDA Toolkit & cuDNN (如需 GPU 加速，根据具体项目要求安装对应版本)\n * 包管理器：`pip` 或 `conda`\n\n**推荐国内加速方案**：\n在安装 Python 依赖时，建议使用清华或阿里镜像源以提升下载速度。\n```bash\n# 临时使用清华镜像源\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple \u003Cpackage_name>\n```\n\n## 获取与筛选项目\n\n该工具的核心是浏览其维护的列表页面，找到你感兴趣的项目仓库链接。\n\n1. **访问列表**：\n 前往 GitHub 仓库页面查看最新的排行榜（原内容中提供的链接为历史快照，请搜索 `mbadry1\u002FTrending-Deep-Learning` 获取最新数据）。\n \n2. **筛选项目**：\n 根据 `Description`（描述）和 `Language`（语言）列，找到符合你需求的项目。例如：\n * 想学强化学习：关注 `spinningup` 或 `ray`。\n * 想做语音合成：关注 `Real-Time-Voice-Cloning`。\n * 想找中文教程：关注 `Deep-Learning-with-TensorFlow-book` 或 `Dive-into-DL-PyTorch`。\n\n## 安装与运行示例\n\n一旦在列表中选定目标项目（以排名靠前的 **Real-Time-Voice-Cloning** 为例），请按照以下通用步骤操作。*注意：每个具体项目的安装命令可能不同，请务必以该项目自身的 README 为准。*\n\n### 1. 克隆代码库\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FCorentinJ\u002FReal-Time-Voice-Cloning.git\ncd Real-Time-Voice-Cloning\n```\n\n### 2. 创建虚拟环境 (推荐)\n```bash\npython -m venv venv\nsource venv\u002Fbin\u002Factivate # Linux\u002FmacOS\n# venv\\Scripts\\activate # Windows\n```\n\n### 3. 安装依赖\n大多数项目提供 `requirements.txt`。使用国内镜像加速安装：\n```bash\npip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n*若项目包含自定义 C++ 扩展（如某些 PyTorch 插件），可能需要额外运行 `python setup.py install`。*\n\n### 4. 下载预训练模型\n许多深度学习项目需要预训练权重才能运行。通常在项目 README 中有下载链接或自动下载脚本。\n```bash\n# 示例：运行项目自带的下载脚本（具体命令视项目而定）\npython demo_cli.py --models_dir .\u002Fpretrained_models\n```\n\n### 5. 基本运行\n执行项目的入口脚本进行测试。\n```bash\n# 示例：启动实时语音克隆演示\npython demo_cli.py\n```\n\n## 常用热门项目快速指引\n\n根据列表内容，以下是几个典型项目的快速启动方向：\n\n* **Spinningup (OpenAI)**: 适合学习深度强化学习。\n ```bash\n git clone https:\u002F\u002Fgithub.com\u002Fopenai\u002Fspinningup.git\n pip install -e .\n ```\n* **Dive-into-DL-PyTorch**: 中文深度学习教程，无需复杂安装，直接打开 Jupyter Notebook 运行。\n ```bash\n git clone https:\u002F\u002Fgithub.com\u002FShusenTang\u002FDive-into-DL-PyTorch.git\n jupyter notebook\n ```\n* **Ray**: 分布式计算框架。\n ```bash\n pip install ray[default] -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n ```\n\n**提示**：列表中的项目更新频繁，部分新项目（标记为 `:new:`）可能文档尚不完善，建议优先选择 Star 总数较高且近期活跃的项目进行尝试。","某 AI 初创公司的算法团队正急需为新的语音合成项目寻找高效的开源基线模型，以缩短研发周期。\n\n### 没有 Trending-Deep-Learning 时\n- **信息筛选低效**：工程师需在 GitHub 海量仓库中手动搜索\"voice cloning\"或\"RNN\"等关键词，耗费数小时浏览大量过时或低质量项目。\n- **错失前沿技术**：难以区分哪些是近期突然爆发的新星项目（如当日获星激增的 `Real-Time-Voice-Cloning`），容易固守旧有方案而忽略更优解。\n- **验证成本高昂**：缺乏按“单日获星数”排序的直观指标，团队不得不下载多个仓库进行试错，才能确认哪个社区活跃度最高、维护最及时。\n- **视野受限**：容易遗漏跨领域的创新工具，例如原本只关注语音，却可能错过像 `spinningup` 这样对强化学习策略优化有启发的教育资源。\n\n### 使用 Trending-Deep-Learning 后\n- **精准锁定热点**：直接查看按单日获星排序的榜单，瞬间发现 `Real-Time-Voice-Cloning` 等高分项目，将选型时间从数小时压缩至几分钟。\n- **把握技术风向**：通过识别榜单中的\":new:\"标记项目（如 `pytorch3d`），团队能第一时间掌握社区最新动向，确保技术栈不落后。\n- **降低决策风险**：依据“今日获星数”这一实时热度指标，快速判断项目的社区认可度和更新频率，优先选择高活跃度的仓库作为基线。\n- **拓展技术边界**：榜单自动聚合了 CNN、RNN 等多类深度学习资源，帮助团队意外发现 `ray` 等分布式训练框架，优化了整体架构设计。\n\nTrending-Deep-Learning 将被动的大海捞针转变为主动的趋势捕捉，让开发者始终站在深度学习创新的最前沿。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmbadry1_Trending-Deep-Learning_f950cbe3.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",[83],{"name":84,"color":85,"percentage":86},"Python","#3572A5",100,674,222,"2026-04-01T15:11:21","MIT",1,"","未说明",{"notes":95,"python":93,"dependencies":96},"该仓库并非一个可运行的 AI 工具或框架，而是一份关于深度学习领域热门 GitHub 仓库的统计列表（基于 2020 年 2 月 2 日的数据）。README 内容仅展示了排名、项目名称、描述及星级增长情况，不包含任何代码安装指南、环境配置要求或依赖库信息。列表中提及的项目（如 PyTorch, TensorFlow, DeepFaceLab 等）各自有独立的环境需求，需参考其原始仓库文档。",[],[14],[99,100,101,102,103,104,105,106,107,108],"deep-learning","deep-neural-networks","trending-repositories","convolutional-neural-networks","recurrent-neural-networks","deep-reinforcement-learning","stargazers-count","artificial-neural-networks","artificial-intelligence","machine-learning","2026-03-27T02:49:30.150509","2026-04-19T03:03:13.067932",[],[]]