[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-metrofun--machine-learning-surveys":3,"tool-metrofun--machine-learning-surveys":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",140436,2,"2026-04-05T23:32:43",[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":78,"owner_twitter":78,"owner_website":81,"owner_url":82,"languages":83,"stars":88,"forks":89,"last_commit_at":90,"license":78,"difficulty_score":91,"env_os":92,"env_gpu":93,"env_ram":93,"env_deps":94,"category_tags":97,"github_topics":98,"view_count":10,"oss_zip_url":78,"oss_zip_packed_at":78,"status":16,"created_at":103,"updated_at":104,"faqs":105,"releases":106},2626,"metrofun\u002Fmachine-learning-surveys","machine-learning-surveys","A curated list of Machine Learning Surveys, Tutorials and Books.","machine-learning-surveys 是一个精心整理的机器学习资源库，汇集了该领域高质量的综述论文、教程和经典书籍。面对机器学习技术迭代快、细分方向多（如深度学习、自然语言处理、强化学习等）导致的文献检索难、入门门槛高等问题，它提供了一份结构清晰的知识地图，帮助用户快速定位特定主题的核心资料。\n\n这份资源库特别适合研究人员、学生以及希望系统提升理论水平的开发者使用。无论是需要撰写文献综述的学者，还是想要深入理解聚类、分类或生物信息学等具体算法的工程师，都能在这里找到权威的参考指引。其独特亮点在于覆盖范围极广，从基础的无监督学习到前沿的多视图学习均有涉猎，且每条资源都标注了作者、年份及篇幅，部分经典内容还配有特别推荐标记。通过 machine-learning-surveys，用户可以高效地站在巨人的肩膀上，避免在海量信息中盲目摸索，从而更专注于技术本身的探索与创新。","\u003C!--\n    This is an auto-generated file. Please check \"How to Contribute\" wiki\n    here: https:\u002F\u002Fgithub.com\u002Fmetrofun\u002Fmachine-learning-surveys\u002Fwiki\u002FHow-to-Contribute\n-->\n# Machine Learning Surveys\n\nA curated list of Machine Learning related surveys, overviews and books.\n\nIf you want to contribute to this list (please do), check [How to Contribute](https:\u002F\u002Fgithub.com\u002Fmetrofun\u002Fmachine-learning-surveys\u002Fwiki\u002FHow-to-Contribute-a-Paper) wiki or contact me [@ML_Review](https:\u002F\u002Ftwitter.com\u002FML_Review).\n\n## Table of Contents\n\n- [Active Learning](#active-learning)\n- [Bioinformatics](#bioinformatics)\n- [Classification](#classification)\n- [Clustering](#clustering)\n- [Computer Vision](#computer-vision)\n- [Deep Learning](#deep-learning)\n- [Dimensionality Reduction](#dimensionality-reduction)\n- [Ensemble Learning](#ensemble-learning)\n- [Metric Learning](#metric-learning)\n- [Monte Carlo](#monte-carlo)\n- [Multi-Armed Bandit](#multi-armed-bandit)\n- [Multi-View Learning](#multi-view-learning)\n- [Natural Language Processing](#natural-language-processing)\n- [Physics](#physics)\n- [Probabilistic Models](#probabilistic-models)\n- [Recommender Systems](#recommender-systems)\n- [Reinforcement Learning](#reinforcement-learning)\n- [Robotics](#robotics)\n- [Semi-Supervised Learning](#semi-supervised-learning)\n- [Submodular Functions](#submodular-functions)\n- [Transfer Learning](#transfer-learning)\n- [Unsupervised Learning](#unsupervised-learning)\n\n\n### Active Learning\n\n* [Active Learning Literature Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Active%20Learning%20Literature%20Survey%22%20author%3A%22B%20Settles%22 \"B Settles\") (2010)\n[B Settles] [67pp]  \n\n### Bioinformatics\n\n* [Introduction to Bioinformatics](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Introduction%20to%20Bioinformatics%22%20author%3A%22A%20Lesk%22 \"A Lesk\") (2013)\n[A Lesk] [255pp]  📚 \n* [Bioinformatics - an Introduction for Computer Scientists](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Bioinformatics%20-%20an%20Introduction%20for%20Computer%20Scientists%22%20author%3A%22J%20Cohen%22 \"J Cohen\") (2004)\n[J Cohen] [37pp]  \n* [Opportunities and Obstacles for Deep Learning in Biology and Medicine](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opportunities%20and%20Obstacles%20for%20Deep%20Learning%20in%20Biology%20and%20Medicine%22%20author%3A%22T%20Ching%22 \"T Ching, DS Himmelstein, BK Beaulieu-jones\") (2017)\n[T Ching, DS Himmelstein, BK Beaulieu-jones] [102pp]  \n\n### Classification\n\n* [Supervised Machine Learning: A Review of Classification Techniques](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Supervised%20Machine%20Learning%3A%20A%20Review%20of%20Classification%20Techniques%22%20author%3A%22SB%20Kotsiantis%22 \"SB Kotsiantis, I Zaharakis, P Pintelas\") (2007)\n[SB Kotsiantis, I Zaharakis, P Pintelas] [20pp]  \n* [Web Page Classification: Features and Algorithms](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Web%20Page%20Classification%3A%20Features%20and%20Algorithms%22%20author%3A%22X%20Qi%22 \"X Qi, BD Davison\") (2009)\n[X Qi, BD Davison] [31pp]  \n\n### Clustering\n\n* [Data Clustering: 50 Years Beyond K-Means](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Data%20Clustering%3A%2050%20Years%20Beyond%20K-Means%22%20author%3A%22AK%20Jain%22 \"AK Jain\") (2010)\n[AK Jain] [16pp]  ⭐\n* [A Tutorial on Spectral Clustering](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Tutorial%20on%20Spectral%20Clustering%22%20author%3A%22U%20VON%20Luxburg%22 \"U VON Luxburg\") (2007)\n[U VON Luxburg] [32pp]  \n* [Handbook of Blind Source Separation: Independent Component Analysis and Applications](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Handbook%20of%20Blind%20Source%20Separation%3A%20Independent%20Component%20Analysis%20and%20Applications%22%20author%3A%22P%20Comon%22 \"P Comon, C Jutten\") (2010)\n[P Comon, C Jutten] [65pp]  📚 \n* [Survey of Clustering Algorithms](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Survey%20of%20Clustering%20Algorithms%22%20author%3A%22R%20Xu%22 \"R Xu, D Wunsch\") (2005)\n[R Xu, D Wunsch] [34pp]  \n* [A Survey of Clustering Data Mining Techniques](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Clustering%20Data%20Mining%20Techniques%22%20author%3A%22P%20Berkhin%22 \"P Berkhin\") (2006)\n[P Berkhin] [56pp]  \n* [Clustering](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Clustering%22%20author%3A%22R%20Xu%22 \"R Xu, D Wunsch\") (2008)\n[R Xu, D Wunsch] [341pp]  📚 \n\n### Computer Vision\n\n* [Pedestrian Detection: An Evaluation of the State of the Art](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Pedestrian%20Detection%3A%20An%20Evaluation%20of%20the%20State%20of%20the%20Art%22%20author%3A%22P%20Dollar%22 \"P Dollar, C Wojek, B Schiele\") (2012)\n[P Dollar, C Wojek, B Schiele] [19pp]  ⭐\n* [Computer Vision: Algorithms and Applications](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%3A%20Algorithms%20and%20Applications%22%20author%3A%22R%20Szeliski%22 \"R Szeliski\") (2010)\n[R Szeliski] [874pp]  📚 ⭐\n* [A Survey of Appearance Models in Visual Object Tracking](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Appearance%20Models%20in%20Visual%20Object%20Tracking%22%20author%3A%22X%20Li%22 \"X Li\") (2013)\n[X Li] [42pp]  ⭐\n* [Object Tracking: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Object%20Tracking%3A%20A%20Survey%22%20author%3A%22A%20Yilmaz%22 \"A Yilmaz\") (2006)\n[A Yilmaz] [45pp]  \n* [Head Pose Estimation in Computer Vision: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Head%20Pose%20Estimation%20in%20Computer%20Vision%3A%20A%20Survey%22%20author%3A%22E%20Murphy-chutorian%22 \"E Murphy-chutorian, MM Trivedi\") (2009)\n[E Murphy-chutorian, MM Trivedi] [20pp]  \n* [A Survey of Recent Advances in Face Detection](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Recent%20Advances%20in%20Face%20Detection%22%20author%3A%22C%20Zhang%22 \"C Zhang, Z Zhang\") (2010)\n[C Zhang, Z Zhang] [17pp]  \n* [Monocular Model-Based 3d Tracking of Rigid Objects: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Monocular%20Model-Based%203d%20Tracking%20of%20Rigid%20Objects%3A%20A%20Survey%22%20author%3A%22V%20Lepetit%22 \"V Lepetit\") (2005)\n[V Lepetit] [91pp]  \n* [A Survey on Face Detection in the Wild: Past, Present and Future](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Face%20Detection%20in%20the%20Wild%3A%20Past%2C%20Present%20and%20Future%22%20author%3A%22S%20Zafeiriou%22 \"S Zafeiriou, C Zhang, Z Zhang\") (2015)\n[S Zafeiriou, C Zhang, Z Zhang] [50pp]  \n* [A Review on Deep Learning Techniques Applied to Semantic Segmentation](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Review%20on%20Deep%20Learning%20Techniques%20Applied%20to%20Semantic%20Segmentation%22%20author%3A%22A%20Garcia-garcia%22 \"A Garcia-garcia, S Orts-escolano\") (2017)\n[A Garcia-garcia, S Orts-escolano] [23pp]  \n* [Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%20for%20Autonomous%20Vehicles%3A%20Problems%2C%20Datasets%20and%20State-of-the-Art%22%20author%3A%22D%20Russo%22 \"D Russo, B VAN Roy, A Kazerouni, I Osband\") (2017)\n[D Russo, B VAN Roy, A Kazerouni, I Osband] [67pp]  \n* [Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%20for%20Autonomous%20Vehicles%3A%20Problems%2C%20Datasets%20and%20State-of-the-Art%22%20author%3A%22J%20Janai%22 \"J Janai, F Güney, A Behl, A Geiger\") (2017)\n[J Janai, F Güney, A Behl, A Geiger] [14pp]  \n\n### Deep Learning\n\n* [Deep Learning](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%22%20author%3A%22IJ%20Goodfellow%22 \"IJ Goodfellow, Y Bengio, A Courville\") (2016)\n[IJ Goodfellow, Y Bengio, A Courville] [800pp]  📚 ⭐⭐\n* [Deep Learning in Neural Networks: An Overview](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20in%20Neural%20Networks%3A%20An%20Overview%22%20author%3A%22J%20Schmidhuber%22 \"J Schmidhuber\") (2015)\n[J Schmidhuber] [88pp]  ⭐⭐\n* [Learning Deep Architectures for Ai](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Learning%20Deep%20Architectures%20for%20Ai%22%20author%3A%22Y%20Bengio%22 \"Y Bengio\") (2009)\n[Y Bengio] [71pp]  ⭐\n* [Tutorial on Variational Autoencoders](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Tutorial%20on%20Variational%20Autoencoders%22%20author%3A%22C%20Doersch%22 \"C Doersch\") (2016)\n[C Doersch] [65pp]  ⭐\n* [Deep Reinforcement Learning: An Overview](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Reinforcement%20Learning%3A%20An%20Overview%22%20author%3A%22%20Y%20Li%22 \" Y Li\") (2017)\n[ Y Li] [30pp]  \n* [NIPS 2016 Tutorial: Generative Adversarial Networks](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22NIPS%202016%20Tutorial%3A%20Generative%20Adversarial%20Networks%22%20author%3A%22I%20Goodfellow%22 \"I Goodfellow\") (2016)\n[I Goodfellow] [57pp]  \n* [Opportunities and Obstacles for Deep Learning in Biology and Medicine](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opportunities%20and%20Obstacles%20for%20Deep%20Learning%20in%20Biology%20and%20Medicine%22%20author%3A%22T%20Ching%22 \"T Ching, DS Himmelstein, BK Beaulieu-jones\") (2017)\n[T Ching, DS Himmelstein, BK Beaulieu-jones] [102pp]  \n* [A Review on Deep Learning Techniques Applied to Semantic Segmentation](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Review%20on%20Deep%20Learning%20Techniques%20Applied%20to%20Semantic%20Segmentation%22%20author%3A%22A%20Garcia-garcia%22 \"A Garcia-garcia, S Orts-escolano\") (2017)\n[A Garcia-garcia, S Orts-escolano] [23pp]  \n* [Deep Learning for Video Game Playing](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20for%20Video%20Game%20Playing%22%20author%3A%22N%20Justesen%22 \"N Justesen, P Bontrager, J Togelius, S Risi\") (2017)\n[N Justesen, P Bontrager, J Togelius, S Risi] [16pp]  \n* [Deep Learning Techniques for Music Generation](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20Techniques%20for%20Music%20Generation%22%20author%3A%22JP%20Briot%22 \"JP Briot, G Hadjeres, F PACHET \") (2017)\n[JP Briot, G Hadjeres, F PACHET ] [108pp]  \n\n### Dimensionality Reduction\n\n* [Dimensionality Reduction: A Comparative Review](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Dimensionality%20Reduction%3A%20A%20Comparative%20Review%22%20author%3A%22L%20VAN%20DER%20Maaten%22 \"L VAN DER Maaten, E Postma\") (2009)\n[L VAN DER Maaten, E Postma] [36pp]  \n* [Dimension Reduction: A Guided Tour](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Dimension%20Reduction%3A%20A%20Guided%20Tour%22%20author%3A%22CJC%20Burges%22 \"CJC Burges\") (2010)\n[CJC Burges] [64pp]  \n\n### Ensemble Learning\n\n* [Ensemble Methods: Foundations and Algorithms](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Ensemble%20Methods%3A%20Foundations%20and%20Algorithms%22%20author%3A%22ZH%20Zhou%22 \"ZH Zhou\") (2012)\n[ZH Zhou] [234pp]  \n* [Ensemble Approaches for Regression: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Ensemble%20Approaches%20for%20Regression%3A%20A%20Survey%22%20author%3A%22J%20Mendes-moreira%22 \"J Mendes-moreira, C Soares, AM Jorge\") (2012)\n[J Mendes-moreira, C Soares, AM Jorge] [40pp]  \n\n### Metric Learning\n\n* [A Survey on Metric Learning for Feature Vectors and Structured Data](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Metric%20Learning%20for%20Feature%20Vectors%20and%20Structured%20Data%22%20author%3A%22A%20Bellet%22 \"A Bellet\") (2014)\n[A Bellet] [59pp]  \n* [Metric Learning: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Metric%20Learning%3A%20A%20Survey%22%20author%3A%22B%20Kulis%22 \"B Kulis\") (2012)\n[B Kulis] [80pp]  \n\n### Monte Carlo\n\n* [Geometric Integrators and the Hamiltonian Monte Carlo Method](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Geometric%20Integrators%20and%20the%20Hamiltonian%20Monte%20Carlo%20Method%22%20author%3A%22N%20Bou-rabee%22 \"N Bou-rabee, JM Sanz-serna\") (2017)\n[N Bou-rabee, JM Sanz-serna] [92pp]  \n\n### Multi-Armed Bandit\n\n* [Regret Analysis of Stochastic and Nonstochastic Multi-Armed Bandit Problems](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Regret%20Analysis%20of%20Stochastic%20and%20Nonstochastic%20Multi-Armed%20Bandit%20Problems%22%20author%3A%22S%20Bubeck%22 \"S Bubeck, N Cesa-bianchi\") (2012)\n[S Bubeck, N Cesa-bianchi] [130pp]  ⭐\n* [A Survey of Online Experiment Design With the Stochastic Multi-Armed Bandit](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Online%20Experiment%20Design%20With%20the%20Stochastic%20Multi-Armed%20Bandit%22%20author%3A%22G%20Burtini%22 \"G Burtini, J Loeppky, R Lawrence\") (2015)\n[G Burtini, J Loeppky, R Lawrence] [49pp]  \n* [A Tutorial on Thompson Sampling](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Tutorial%20on%20Thompson%20Sampling%22%20author%3A%22D%20Russo%22 \"D Russo, B VAN Roy, A Kazerouni, I Osband\") (2017)\n[D Russo, B VAN Roy, A Kazerouni, I Osband] [39pp]  \n\n### Multi-View Learning\n\n* [A Survey on Multi-View Learning](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Multi-View%20Learning%22%20author%3A%22C%20Xu%22 \"C Xu\") (2013)\n[C Xu] [59pp]  \n* [A Survey of Multi-View Machine Learning](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Multi-View%20Machine%20Learning%22%20author%3A%22S%20Sun%22 \"S Sun\") (2013)\n[S Sun] [13pp]  \n\n### Natural Language Processing\n\n* [A Primer on Neural Network Models for Natural Language Processing](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Primer%20on%20Neural%20Network%20Models%20for%20Natural%20Language%20Processing%22%20author%3A%22Y%20Goldberg%22 \"Y Goldberg\") (2016)\n[Y Goldberg] [76pp]  ⭐\n* [Probabilistic Topic Models](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Probabilistic%20Topic%20Models%22%20author%3A%22DM%20Blei%22 \"DM Blei\") (2012)\n[DM Blei] [16pp]  ⭐\n* [Natural Language Processing (Almost) From Scratch](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Natural%20Language%20Processing%20%28Almost%29%20From%20Scratch%22%20author%3A%22R%20Collobert%22 \"R Collobert\") (2011)\n[R Collobert] [45pp]  ⭐\n* [Opinion Mining and Sentiment Analysis](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opinion%20Mining%20and%20Sentiment%20Analysis%22%20author%3A%22B%20Pang%22 \"B Pang, L Lee\") (2008)\n[B Pang, L Lee] [94pp]  ⭐\n* [Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Survey%20of%20the%20State%20of%20the%20Art%20in%20Natural%20Language%20Generation%3A%20Core%20Tasks%2C%20Applications%20and%20Evaluation%22%20author%3A%22A%20Gatt%22 \"A Gatt, E Krahmer\") (2017)\n[A Gatt, E Krahmer] [111pp]  ⭐\n* [Opinion Mining and Sentiment Analysis](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opinion%20Mining%20and%20Sentiment%20Analysis%22%20author%3A%22B%20Liu%22 \"B Liu, L Zhang\") (2012)\n[B Liu, L Zhang] [38pp]  \n* [Neural Machine Translation and Sequence-to-Sequence Models: A Tutorial](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Neural%20Machine%20Translation%20and%20Sequence-to-Sequence%20Models%3A%20A%20Tutorial%22%20author%3A%22G%20Neubig%22 \"G Neubig\") (2017)\n[G Neubig] [65pp]  \n* [Machine Learning in Automated Text Categorization](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Machine%20Learning%20in%20Automated%20Text%20Categorization%22%20author%3A%22F%20Sebastiani%22 \"F Sebastiani\") (2002)\n[F Sebastiani] [55pp]  \n* [Statistical Machine Translation](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Statistical%20Machine%20Translation%22%20author%3A%22P%20Koehn%22 \"P Koehn\") (2009)\n[P Koehn] [149pp]  📚 \n* [Statistical Machine Translation](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Statistical%20Machine%20Translation%22%20author%3A%22A%20Lopez%22 \"A Lopez\") (2008)\n[A Lopez] [55pp]  \n* [Machine Transliteration Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Machine%20Transliteration%20Survey%22%20author%3A%22S%20Karimi%22 \"S Karimi, F Scholer, A Turpin\") (2011)\n[S Karimi, F Scholer, A Turpin] [46pp]  \n* [Neural Machine Translation and Sequence-to-Sequence Models: A Tutorial](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Neural%20Machine%20Translation%20and%20Sequence-to-Sequence%20Models%3A%20A%20Tutorial%22%20author%3A%22G%20Neubig%22 \"G Neubig\") (2017)\n[G Neubig] [57pp]  \n\n### Physics\n\n* [Machine Learning & Artificial Intelligence in the Quantum Domain](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Machine%20Learning%20%26%20Artificial%20Intelligence%20in%20the%20Quantum%20Domain%22%20author%3A%22V%20Dunjko%22 \"V Dunjko, HJ Briegel\") (2017)\n[V Dunjko, HJ Briegel] [106pp]  \n\n### Probabilistic Models\n\n* [Graphical Models, Exponential Families, and Variational Inference](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Graphical%20Models%2C%20Exponential%20Families%2C%20and%20Variational%20Inference%22%20author%3A%22MJ%20Wainwright%22 \"MJ Wainwright, MI Jordan\") (2008)\n[MJ Wainwright, MI Jordan] [305pp]  \n* [An Introduction to Conditional Random Fields](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22An%20Introduction%20to%20Conditional%20Random%20Fields%22%20author%3A%22C%20Sutton%22 \"C Sutton\") (2011)\n[C Sutton] [90pp]  \n* [An Introduction to Conditional Random Fields for Relational Learning](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22An%20Introduction%20to%20Conditional%20Random%20Fields%20for%20Relational%20Learning%22%20author%3A%22C%20Sutton%22 \"C Sutton\") (2006)\n[C Sutton] [35pp]  \n* [An Introduction to Mcmc for Machine Learning](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22An%20Introduction%20to%20Mcmc%20for%20Machine%20Learning%22%20author%3A%22C%20Andrieu%22 \"C Andrieu, N DE Freitas, A Doucet, MI Jordan\") (2003)\n[C Andrieu, N DE Freitas, A Doucet, MI Jordan] [39pp]  \n* [Introduction to Probability Models](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Introduction%20to%20Probability%20Models%22%20author%3A%22SM%20Ross%22 \"SM Ross\") (2014)\n[SM Ross] [801pp]  📚 \n\n### Recommender Systems\n\n* [Introduction to Recommender Systems Handbook](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Introduction%20to%20Recommender%20Systems%20Handbook%22%20author%3A%22F%20Ricci%22 \"F Ricci, L Rokach, B Shapira\") (2011)\n[F Ricci, L Rokach, B Shapira] [845pp]  📚 ⭐\n* [Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Toward%20the%20Next%20Generation%20of%20Recommender%20Systems%3A%20A%20Survey%20of%20the%20State-of-the-Art%20and%20Possible%20Extensions%22%20author%3A%22G%20Adomavicius%22 \"G Adomavicius, A Tuzhilin\") (2008)\n[G Adomavicius, A Tuzhilin] [43pp]  ⭐\n* [Matrix Factorization Techniques for Recommender Systems](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Matrix%20Factorization%20Techniques%20for%20Recommender%20Systems%22%20author%3A%22Y%20Koren%22 \"Y Koren, R Bell, C Volinsky\") (2009)\n[Y Koren, R Bell, C Volinsky] [8pp]  ⭐\n* [A Survey of Collaborative Filtering Techniques](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Collaborative%20Filtering%20Techniques%22%20author%3A%22X%20Su%22 \"X Su, TM Khoshgoftaar\") (2009)\n[X Su, TM Khoshgoftaar] [20pp]  \n\n### Reinforcement Learning\n\n* [Reinforcement Learning in Robotics: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%20in%20Robotics%3A%20A%20Survey%22%20author%3A%22J%20Kober%22 \"J Kober, JA Bagnell, J Peterskober\") (2013)\n[J Kober, JA Bagnell, J Peterskober] [74pp]  ⭐\n* [Deep Reinforcement Learning: An Overview](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Reinforcement%20Learning%3A%20An%20Overview%22%20author%3A%22%20Y%20Li%22 \" Y Li\") (2017)\n[ Y Li] [30pp]  \n* [Reinforcement Learning: An Introduction](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%3A%20An%20Introduction%22%20author%3A%22RS%20Sutton%22 \"RS Sutton, AG Barto\") (2016)\n[RS Sutton, AG Barto] [398pp]  📚 \n* [Bayesian Reinforcement Learning: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Bayesian%20Reinforcement%20Learning%3A%20A%20Survey%22%20author%3A%22M%20Ghavamzadeh%22 \"M Ghavamzadeh, S Mannor, J Pineau\") (2016)\n[M Ghavamzadeh, S Mannor, J Pineau] [147pp]  \n* [Reinforcement Learning: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%3A%20A%20Survey%22%20author%3A%22LP%20Kaelbling%22 \"LP Kaelbling, ML Littman, AW Moore\") (1996)\n[LP Kaelbling, ML Littman, AW Moore] [49pp]  \n* [Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%20for%20Autonomous%20Vehicles%3A%20Problems%2C%20Datasets%20and%20State-of-the-Art%22%20author%3A%22J%20Janai%22 \"J Janai, F Güney, A Behl, A Geiger\") (2017)\n[J Janai, F Güney, A Behl, A Geiger] [14pp]  \n* [Deep Learning for Video Game Playing](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20for%20Video%20Game%20Playing%22%20author%3A%22N%20Justesen%22 \"N Justesen, P Bontrager, J Togelius, S Risi\") (2017)\n[N Justesen, P Bontrager, J Togelius, S Risi] [16pp]  \n\n### Robotics\n\n* [Reinforcement Learning in Robotics: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%20in%20Robotics%3A%20A%20Survey%22%20author%3A%22J%20Kober%22 \"J Kober, JA Bagnell, J Peterskober\") (2013)\n[J Kober, JA Bagnell, J Peterskober] [74pp]  ⭐\n* [A Survey of Robot Learning From Demonstration](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Robot%20Learning%20From%20Demonstration%22%20author%3A%22BD%20Argall%22 \"BD Argall, S Chernova, M Veloso\") (2009)\n[BD Argall, S Chernova, M Veloso] [15pp]  \n\n### Semi-Supervised Learning\n\n* [Semi-Supervised Learning Literature Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Semi-Supervised%20Learning%20Literature%20Survey%22%20author%3A%22X%20Zhu%22 \"X Zhu\") (2008)\n[X Zhu] [59pp]  \n\n### Submodular Functions\n\n* [Learning With Submodular Functions: A Convex Optimization Perspective](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Learning%20With%20Submodular%20Functions%3A%20A%20Convex%20Optimization%20Perspective%22%20author%3A%22F%20Bach%22 \"F Bach\") (2013)\n[F Bach] [173pp]  \n* [Submodular Function Maximization](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Submodular%20Function%20Maximization%22%20author%3A%22A%20Krause%22 \"A Krause, D Golovin\") (2012)\n[A Krause, D Golovin] [28pp]  \n\n### Transfer Learning\n\n* [A Survey on Transfer Learning](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Transfer%20Learning%22%20author%3A%22SJ%20Pan%22 \"SJ Pan, Q Yang\") (2010)\n[SJ Pan, Q Yang] [15pp]  ⭐\n* [Transfer Learning for Reinforcement Learning Domains: A Survey](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Transfer%20Learning%20for%20Reinforcement%20Learning%20Domains%3A%20A%20Survey%22%20author%3A%22ME%20Taylor%22 \"ME Taylor, P Stone\") (2009)\n[ME Taylor, P Stone] [53pp]  \n\n### Unsupervised Learning\n\n* [Tutorial on Variational Autoencoders](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Tutorial%20on%20Variational%20Autoencoders%22%20author%3A%22C%20Doersch%22 \"C Doersch\") (2016)\n[C Doersch] [65pp]  ⭐\n\n","\u003C!--\n    这是一个自动生成的文件。请查看“如何贡献”维基页面：\n    https:\u002F\u002Fgithub.com\u002Fmetrofun\u002Fmachine-learning-surveys\u002Fwiki\u002FHow-to-Contribute\n-->\n# 机器学习综述\n\n一个精心整理的机器学习相关调查、综述和书籍列表。\n\n如果您想为本列表贡献力量（非常欢迎），请查阅 [如何贡献](https:\u002F\u002Fgithub.com\u002Fmetrofun\u002Fmachine-learning-surveys\u002Fwiki\u002FHow-to-Contribute-a-Paper) 维基页面，或联系我 [@ML_Review](https:\u002F\u002Ftwitter.com\u002FML_Review)。\n\n## 目录\n\n- [主动学习](#active-learning)\n- [生物信息学](#bioinformatics)\n- [分类](#classification)\n- [聚类](#clustering)\n- [计算机视觉](#computer-vision)\n- [深度学习](#deep-learning)\n- [降维](#dimensionality-reduction)\n- [集成学习](#ensemble-learning)\n- [度量学习](#metric-learning)\n- [蒙特卡洛方法](#monte-carlo)\n- [多臂老虎机问题](#multi-armed-bandit)\n- [多视图学习](#multi-view-learning)\n- [自然语言处理](#natural-language-processing)\n- [物理学](#physics)\n- [概率模型](#probabilistic-models)\n- [推荐系统](#recommender-systems)\n- [强化学习](#reinforcement-learning)\n- [机器人学](#robotics)\n- [半监督学习](#semi-supervised-learning)\n- [次模函数](#submodular-functions)\n- [迁移学习](#transfer-learning)\n- [无监督学习](#unsupervised-learning)\n\n\n### 主动学习\n\n* [主动学习文献综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Active%20Learning%20Literature%20Survey%22%20author%3A%22B%20Settles%22 \"B Settles\") (2010)\n[B Settles] [67页]  \n\n### 生物信息学\n\n* [生物信息学导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Introduction%20to%20Bioinformatics%22%20author%3A%22A%20Lesk%22 \"A Lesk\") (2013)\n[A Lesk] [255页]  📚 \n* [生物信息学——给计算机科学家的入门](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Bioinformatics%20-%20an%20Introduction%20for%20Computer%20Scientists%22%20author%3A%22J%20Cohen%22 \"J Cohen\") (2004)\n[J Cohen] [37页]  \n* [深度学习在生物学和医学中的机遇与挑战](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opportunities%20and%20Obstacles%20for%20Deep%20Learning%20in%20Biology%20and%20Medicine%22%20author%3A%22T%20Ching%22 \"T Ching, DS Himmelstein, BK Beaulieu-jones\") (2017)\n[T Ching, DS Himmelstein, BK Beaulieu-jones] [102页]  \n\n### 分类\n\n* [监督式机器学习：分类技术综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Supervised%20Machine%20Learning%3A%20A%20Review%20of%20Classification%20Techniques%22%20author%3A%22SB%20Kotsiantis%22 \"SB Kotsiantis, I Zaharakis, P Pintelas\") (2007)\n[SB Kotsiantis, I Zaharakis, P Pintelas] [20页]  \n* [网页分类：特征与算法](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Web%20Page%20Classification%3A%20Features%20and%20Algorithms%22%20author%3A%22X%20Qi%22 \"X Qi, BD Davison\") (2009)\n[X Qi, BD Davison] [31页]  \n\n### 聚类\n\n* [数据聚类：超越K均值的50年](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Data%20Clustering%3A%2050%20Years%20Beyond%20K-Means%22%20author%3A%22AK%20Jain%22 \"AK Jain\") (2010)\n[AK Jain] [16页]  ⭐\n* [谱聚类教程](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Tutorial%20on%20Spectral%20Clustering%22%20author%3A%22U%20VON%20Luxburg%22 \"U VON Luxburg\") (2007)\n[U VON Luxburg] [32页]  \n* [盲源分离手册：独立成分分析及其应用](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Handbook%20of%20Blind%20Source%20Separation%3A%20Independent%20Component%20Analysis%20and%20Applications%22%20author%3A%22P%20Comon%22 \"P Comon, C Jutten\") (2010)\n[P Comon, C Jutten] [65页]  📚 \n* [聚类算法综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Survey%20of%20Clustering%20Algorithms%22%20author%3A%22R%20Xu%22 \"R Xu, D Wunsch\") (2005)\n[R Xu, D Wunsch] [34页]  \n* [聚类数据挖掘技术综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Clustering%20Data%20Mining%20Techniques%22%20author%3A%22P%20Berkhin%22 \"P Berkhin\") (2006)\n[P Berkhin] [56页]  \n* [聚类](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Clustering%22%20author%3A%22R%20Xu%22 \"R Xu, D Wunsch\") (2008)\n[R Xu, D Wunsch] [341页]  📚\n\n### 计算机视觉\n\n* [行人检测：现状评估](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Pedestrian%20Detection%3A%20An%20Evaluation%20of%20the%20State%20of%20the%20Art%22%20author%3A%22P%20Dollar%22 \"P Dollar, C Wojek, B Schiele\") (2012)\n[P Dollar, C Wojek, B Schiele] [19页]  ⭐\n* [计算机视觉：算法与应用](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%3A%20Algorithms%20and%20Applications%22%20author%3A%22R%20Szeliski%22 \"R Szeliski\") (2010)\n[R Szeliski] [874页]  📚 ⭐\n* [视觉目标跟踪中外观模型综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Appearance%20Models%20in%20Visual%20Object%20Tracking%22%20author%3A%22X%20Li%22 \"X Li\") (2013)\n[X Li] [42页]  ⭐\n* [目标跟踪：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Object%20Tracking%3A%20A%20Survey%22%20author%3A%22A%20Yilmaz%22 \"A Yilmaz\") (2006)\n[A Yilmaz] [45页]  \n* [计算机视觉中的头部姿态估计：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Head%20Pose%20Estimation%20in%20Computer%20Vision%3A%20A%20Survey%22%20author%3A%22E%20Murphy-chutorian%22 \"E Murphy-chutorian, MM Trivedi\") (2009)\n[E Murphy-chutorian, MM Trivedi] [20页]  \n* [人脸检测最新进展综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Recent%20Advances%20in%20Face%20Detection%22%20author%3A%22C%20Zhang%22 \"C Zhang, Z Zhang\") (2010)\n[C Zhang, Z Zhang] [17页]  \n* [基于单目模型的刚体三维跟踪：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Monocular%20Model-Based%203d%20Tracking%20of%20Rigid%20Objects%3A%20A%20Survey%22%20author%3A%22V%20Lepetit%22 \"V Lepetit\") (2005)\n[V Lepetit] [91页]  \n* [野外人脸检测综述：过去、现在与未来](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Face%20Detection%20in%20the%20Wild%3A%20Past%2C%20Present%20and%20Future%22%20author%3A%22S%20Zafeiriou%22 \"S Zafeiriou, C Zhang, Z Zhang\") (2015)\n[S Zafeiriou, C Zhang, Z Zhang] [50页]  \n* [应用于语义分割的深度学习技术综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Review%20on%20Deep%20Learning%20Techniques%20Applied%20to%20Semantic%20Segmentation%22%20author%3A%22A%20Garcia-garcia%22 \"A Garcia-garcia, S Orts-escolano\") (2017)\n[A Garcia-garcia, S Orts-escolano] [23页]  \n* [自动驾驶车辆的计算机视觉：问题、数据集及现状](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%20for%20Autonomous%20Vehicles%3A%20Problems%2C%20Datasets%20and%20State-of-the-Art%22%20author%3A%22D%20Russo%22 \"D Russo, B VAN Roy, A Kazerouni, I Osband\") (2017)\n[D Russo, B VAN Roy, A Kazerouni, I Osband] [67页]  \n* [自动驾驶车辆的计算机视觉：问题、数据集及现状](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%20for%20Autonomous%20Vehicles%3A%20Problems%2C%20Datasets%20and%20State-of-the-Art%22%20author%3A%22J%20Janai%22 \"J Janai, F Güney, A Behl, A Geiger\") (2017)\n[J Janai, F Güney, A Behl, A Geiger] [14页]  \n\n### 深度学习\n\n* [深度学习](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%22%20author%3A%22IJ%20Goodfellow%22 \"IJ Goodfellow, Y Bengio, A Courville\") (2016)\n[IJ Goodfellow, Y Bengio, A Courville] [800页]  📚 ⭐⭐\n* [神经网络中的深度学习：概述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20in%20Neural%20Networks%3A%20An%20Overview%22%20author%3A%22J%20Schmidhuber%22 \"J Schmidhuber\") (2015)\n[J Schmidhuber] [88页]  ⭐⭐\n* [为人工智能学习深度架构](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Learning%20Deep%20Architectures%20for%20Ai%22%20author%3A%22Y%20Bengio%22 \"Y Bengio\") (2009)\n[Y Bengio] [71页]  ⭐\n* [变分自编码器教程](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Tutorial%20on%20Variational%20Autoencoders%22%20author%3A%22C%20Doersch%22 \"C Doersch\") (2016)\n[C Doersch] [65页]  ⭐\n* [深度强化学习：概述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Reinforcement%20Learning%3A%20An%20Overview%22%20author%3A%22%20Y%20Li%22 \" Y Li\") (2017)\n[ Y Li] [30页]  \n* [NIPS 2016教程：生成对抗网络](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22NIPS%202016%20Tutorial%3A%20Generative%20Adversarial%20Networks%22%20author%3A%22I%20Goodfellow%22 \"I Goodfellow\") (2016)\n[I Goodfellow] [57页]  \n* [生物学和医学领域深度学习的机遇与挑战](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opportunities%20and%20Obstacles%20for%20Deep%20Learning%20in%20Biology%20and%20Medicine%22%20author%3A%22T%20Ching%22 \"T Ching, DS Himmelstein, BK Beaulieu-jones\") (2017)\n[T Ching, DS Himmelstein, BK Beaulieu-jones] [102页]  \n* [应用于语义分割的深度学习技术综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Review%20on%20Deep%20Learning%20Techniques%20Applied%20to%20Semantic%20Segmentation%22%20author%3A%22A%20Garcia-garcia%22 \"A Garcia-garcia, S Orts-escolano\") (2017)\n[A Garcia-garcia, S Orts-escolano] [23页]  \n* [深度学习在视频游戏中的应用](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20for%20Video%20Game%20Playing%22%20author%3A%22N%20Justesen%22 \"N Justesen, P Bontrager, J Togelius, S Risi\") (2017)\n[N Justesen, P Bontrager, J Togelius, S Risi] [16页]  \n* [用于音乐创作的深度学习技术](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20Techniques%20for%20Music%20Generation%22%20author%3A%22JP%20Briot%22 \"JP Briot, G Hadjeres, F PACHET \") (2017)\n[JP Briot, G Hadjeres, F PACHET ] [108页]  \n\n### 降维\n\n* [降维：比较研究](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Dimensionality%20Reduction%3A%20A%20Comparative%20Review%22%20author%3A%22L%20VAN%20DER%20Maaten%22 \"L VAN DER Maaten, E Postma\") (2009)\n[L VAN DER Maaten, E Postma] [36页]  \n* [降维：导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Dimension%20Reduction%3A%20A%20Guided%20Tour%22%20author%3A%22CJC%20Burges%22 \"CJC Burges\") (2010)\n[CJC Burges] [64页]  \n\n### 集成学习\n\n* [集成方法：基础与算法](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Ensemble%20Methods%3A%20Foundations%20and%20Algorithms%22%20author%3A%22ZH%20Zhou%22 \"ZH Zhou\") (2012)\n[ZH Zhou] [234页]  \n* [回归任务中的集成方法：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Ensemble%20Approaches%20for%20Regression%3A%20A%20Survey%22%20author%3A%22J%20Mendes-moreira%22 \"J Mendes-moreira, C Soares, AM Jorge\") (2012)\n[J Mendes-moreira, C Soares, AM Jorge] [40页]\n\n### 度量学习\n\n* [特征向量与结构化数据的度量学习综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Metric%20Learning%20for%20Feature%20Vectors%20and%20Structured%20Data%22%20author%3A%22A%20Bellet%22 \"A Bellet\") (2014)\n[A Bellet] [59页]  \n* [度量学习：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Metric%20Learning%3A%20A%20Survey%22%20author%3A%22B%20Kulis%22 \"B Kulis\") (2012)\n[B Kulis] [80页]  \n\n### 蒙特卡洛方法\n\n* [几何积分器与哈密顿蒙特卡洛方法](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Geometric%20Integrators%20and%20the%20Hamiltonian%20Monte%20Carlo%20Method%22%20author%3A%22N%20Bou-rabee%22 \"N Bou-rabee, JM Sanz-serna\") (2017)\n[N Bou-rabee, JM Sanz-serna] [92页]  \n\n### 多臂老虎机问题\n\n* [随机与非随机多臂老虎机问题的遗憾分析](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Regret%20Analysis%20of%20Stochastic%20and%20Nonstochastic%20Multi-Armed%20Bandit%20Problems%22%20author%3A%22S%20Bubeck%22 \"S Bubeck, N Cesa-bianchi\") (2012)\n[S Bubeck, N Cesa-bianchi] [130页]  ⭐\n* [基于随机多臂老虎机的在线实验设计综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Online%20Experiment%20Design%20With%20the%20Stochastic%20Multi-Armed%20Bandit%22%20author%3A%22G%20Burtini%22 \"G Burtini, J Loeppky, R Lawrence\") (2015)\n[G Burtini, J Loeppky, R Lawrence] [49页]  \n* [汤普森采样教程](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Tutorial%20on%20Thompson%20Sampling%22%20author%3A%22D%20Russo%22 \"D Russo, B VAN Roy, A Kazerouni, I Osband\") (2017)\n[D Russo, B VAN Roy, A Kazerouni, I Osband] [39页]  \n\n### 多视图学习\n\n* [多视图学习综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Multi-View%20Learning%22%20author%3A%22C%20Xu%22 \"C Xu\") (2013)\n[C Xu] [59页]  \n* [多视图机器学习综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Multi-View%20Machine%20Learning%22%20author%3A%22S%20Sun%22 \"S Sun\") (2013)\n[S Sun] [13页]  \n\n### 自然语言处理\n\n* [自然语言处理神经网络模型入门](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Primer%20on%20Neural%20Network%20Models%20for%20Natural%20Language%20Processing%22%20author%3A%22Y%20Goldberg%22 \"Y Goldberg\") (2016)\n[Y Goldberg] [76页]  ⭐\n* [概率主题模型](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Probabilistic%20Topic%20Models%22%20author%3A%22DM%20Blei%22 \"DM Blei\") (2012)\n[DM Blei] [16页]  ⭐\n* [几乎从零开始的自然语言处理](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Natural%20Language%20Processing%20%28Almost%29%20From%20Scratch%22%20author%3A%22R%20Collobert%22 \"R Collobert\") (2011)\n[R Collobert] [45页]  ⭐\n* [观点挖掘与情感分析](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opinion%20Mining%20and%20Sentiment%20Analysis%22%20author%3A%22B%20Pang%22 \"B Pang, L Lee\") (2008)\n[B Pang, L Lee] [94页]  ⭐\n* [自然语言生成现状综述：核心任务、应用与评估](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Survey%20of%20the%20State%20of%20the%20Art%20in%20Natural%20Language%20Generation%3A%20Core%20Tasks%2C%20Applications%20and%20Evaluation%22%20author%3A%22A%20Gatt%22 \"A Gatt, E Krahmer\") (2017)\n[A Gatt, E Krahmer] [111页]  ⭐\n* [观点挖掘与情感分析](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Opinion%20Mining%20and%20Sentiment%20Analysis%22%20author%3A%22B%20Liu%22 \"B Liu, L Zhang\") (2012)\n[B Liu, L Zhang] [38页]  \n* [神经机器翻译与序列到序列模型：教程](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Neural%20Machine%20Translation%20and%20Sequence-to-Sequence%20Models%3A%20A%20Tutorial%22%20author%3A%22G%20Neubig%22 \"G Neubig\") (2017)\n[G Neubig] [65页]  \n* [自动文本分类中的机器学习](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Machine%20Learning%20in%20Automated%20Text%20Categorization%22%20author%3A%22F%20Sebastiani%22 \"F Sebastiani\") (2002)\n[F Sebastiani] [55页]  \n* [统计机器翻译](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Statistical%20Machine%20Translation%22%20author%3A%22P%20Koehn%22 \"P Koehn\") (2009)\n[P Koehn] [149页]  📚 \n* [统计机器翻译](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Statistical%20Machine%20Translation%22%20author%3A%22A%20Lopez%22 \"A Lopez\") (2008)\n[A Lopez] [55页]  \n* [机器转写综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Machine%20Transliteration%20Survey%22%20author%3A%22S%20Karimi%22 \"S Karimi, F Scholer, A Turpin\") (2011)\n[S Karimi, F Scholer, A Turpin] [46页]  \n* [神经机器翻译与序列到序列模型：教程](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Neural%20Machine%20Translation%20and%20Sequence-to-Sequence%20Models%3A%20A%20Tutorial%22%20author%3A%22G%20Neubig%22 \"G Neubig\") (2017)\n[G Neubig] [57页]  \n\n### 物理学\n\n* [量子领域的机器学习与人工智能](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Machine%20Learning%20%26%20Artificial%20Intelligence%20in%20the%20Quantum%20Domain%22%20author%3A%22V%20Dunjko%22 \"V Dunjko, HJ Briegel\") (2017)\n[V Dunjko, HJ Briegel] [106页]  \n\n### 概率模型\n\n* [图模型、指数族与变分推断](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Graphical%20Models%2C%20Exponential%20Families%2C%20and%20Variational%20Inference%22%20author%3A%22MJ%20Wainwright%22 \"MJ Wainwright, MI Jordan\") (2008)\n[MJ Wainwright, MI Jordan] [305页]  \n* [条件随机场导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22An%20Introduction%20to%20Conditional%20Random%20Fields%22%20author%3A%22C%20Sutton%22 \"C Sutton\") (2011)\n[C Sutton] [90页]  \n* [面向关系学习的条件随机场导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22An%20Introduction%20to%20Conditional%20Random%20Fields%20for%20Relational%20Learning%22%20author%3A%22C%20Sutton%22 \"C Sutton\") (2006)\n[C Sutton] [35页]  \n* [用于机器学习的MCMC导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22An%20Introduction%20to%20Mcmc%20for%20Machine%20Learning%22%20author%3A%22C%20Andrieu%22 \"C Andrieu, N DE Freitas, A Doucet, MI Jordan\") (2003)\n[C Andrieu, N DE Freitas, A Doucet, MI Jordan] [39页]  \n* [概率模型导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Introduction%20to%20Probability%20Models%22%20author%3A%22SM%20Ross%22 \"SM Ross\") (2014)\n[SM Ross] [801页]  📚\n\n### 推荐系统\n\n* [推荐系统手册导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Introduction%20to%20Recommender%20Systems%20Handbook%22%20author%3A%22F%20Ricci%22 \"F Ricci, L Rokach, B Shapira\") (2011)\n[F Ricci, L Rokach, B Shapira] [845页]  📚 ⭐\n* [迈向下一代推荐系统：现状与可能扩展的综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Toward%20the%20Next%20Generation%20of%20Recommender%20Systems%3A%20A%20Survey%20of%20the%20State-of-the-Art%20and%20Possible%20Extensions%22%20author%3A%22G%20Adomavicius%22 \"G Adomavicius, A Tuzhilin\") (2008)\n[G Adomavicius, A Tuzhilin] [43页]  ⭐\n* [用于推荐系统的矩阵分解技术](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Matrix%20Factorization%20Techniques%20for%20Recommender%20Systems%22%20author%3A%22Y%20Koren%22 \"Y Koren, R Bell, C Volinsky\") (2009)\n[Y Koren, R Bell, C Volinsky] [8页]  ⭐\n* [协同过滤技术综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Collaborative%20Filtering%20Techniques%22%20author%3A%22X%20Su%22 \"X Su, TM Khoshgoftaar\") (2009)\n[X Su, TM Khoshgoftaar] [20页]  \n\n### 强化学习\n\n* [机器人中的强化学习：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%20in%20Robotics%3A%20A%20Survey%22%20author%3A%22J%20Kober%22 \"J Kober, JA Bagnell, J Peterskober\") (2013)\n[J Kober, JA Bagnell, J Peterskober] [74页]  ⭐\n* [深度强化学习：概述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Reinforcement%20Learning%3A%20An%20Overview%22%20author%3A%22%20Y%20Li%22 \" Y Li\") (2017)\n[ Y Li] [30页]  \n* [强化学习：导论](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%3A%20An%20Introduction%22%20author%3A%22RS%20Sutton%22 \"RS Sutton, AG Barto\") (2016)\n[RS Sutton, AG Barto] [398页]  📚 \n* [贝叶斯强化学习：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Bayesian%20Reinforcement%20Learning%3A%20A%20Survey%22%20author%3A%22M%20Ghavamzadeh%22 \"M Ghavamzadeh, S Mannor, J Pineau\") (2016)\n[M Ghavamzadeh, S Mannor, J Pineau] [147页]  \n* [强化学习：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%3A%20A%20Survey%22%20author%3A%22LP%20Kaelbling%22 \"LP Kaelbling, ML Littman, AW Moore\") (1996)\n[LP Kaelbling, ML Littman, AW Moore] [49页]  \n* [自动驾驶车辆的计算机视觉：问题、数据集与最新进展](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Computer%20Vision%20for%20Autonomous%20Vehicles%3A%20Problems%2C%20Datasets%20and%20State-of-the-Art%22%20author%3A%22J%20Janai%22 \"J Janai, F Güney, A Behl, A Geiger\") (2017)\n[J Janai, F Güney, A Behl, A Geiger] [14页]  \n* [深度学习在电子游戏中的应用](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Deep%20Learning%20for%20Video%20Game%20Playing%22%20author%3A%22N%20Justesen%22 \"N Justesen, P Bontrager, J Togelius, S Risi\") (2017)\n[N Justesen, P Bontrager, J Togelius, S Risi] [16页]  \n\n### 机器人学\n\n* [机器人中的强化学习：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Reinforcement%20Learning%20in%20Robotics%3A%20A%20Survey%22%20author%3A%22J%20Kober%22 \"J Kober, JA Bagnell, J Peterskober\") (2013)\n[J Kober, JA Bagnell, J Peterskober] [74页]  ⭐\n* [机器人模仿学习综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20of%20Robot%20Learning%20From%20Demonstration%22%20author%3A%22BD%20Argall%22 \"BD Argall, S Chernova, M Veloso\") (2009)\n[BD Argall, S Chernova, M Veloso] [15页]  \n\n### 半监督学习\n\n* [半监督学习文献综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Semi-Supervised%20Learning%20Literature%20Survey%22%20author%3A%22X%20Zhu%22 \"X Zhu\") (2008)\n[X Zhu] [59页]  \n\n### 次模函数\n\n* [利用次模函数进行学习：凸优化视角](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Learning%20With%20Submodular%20Functions%3A%20A%20Convex%20Optimization%20Perspective%22%20author%3A%22F%20Bach%22 \"F Bach\") (2013)\n[F Bach] [173页]  \n* [次模函数最大化](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Submodular%20Function%20Maximization%22%20author%3A%22A%20Krause%22 \"A Krause, D Golovin\") (2012)\n[A Krause, D Golovin] [28页]  \n\n### 迁移学习\n\n* [迁移学习综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22A%20Survey%20on%20Transfer%20Learning%22%20author%3A%22SJ%20Pan%22 \"SJ Pan, Q Yang\") (2010)\n[SJ Pan, Q Yang] [15页]  ⭐\n* [强化学习领域中的迁移学习：综述](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Transfer%20Learning%20for%20Reinforcement%20Learning%20Domains%3A%20A%20Survey%22%20author%3A%22ME%20Taylor%22 \"ME Taylor, P Stone\") (2009)\n[ME Taylor, P Stone] [53页]  \n\n### 无监督学习\n\n* [变分自编码器教程](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=%22Tutorial%20on%20Variational%20Autoencoders%22%20author%3A%22C%20Doersch%22 \"C Doersch\") (2016)\n[C Doersch] [65页]  ⭐","# Machine Learning Surveys 快速上手指南\n\n`machine-learning-surveys` 并非一个需要安装运行的软件库，而是一个**精心整理的机器学习综述、概述和书籍的 curated 列表**。它旨在为研究者和开发者提供各细分领域的权威文献索引。\n\n因此，本指南将指导你如何访问、浏览及利用该资源，而非执行传统的安装命令。\n\n## 环境准备\n\n本项目无需特定的操作系统或编程环境依赖。你只需要：\n- **网络连接**：用于访问 GitHub 仓库及链接到的学术资源（如 Google Scholar）。\n- **浏览器**：推荐使用 Chrome、Edge 或 Firefox 以最佳体验阅读 Markdown 渲染页面。\n- **Git (可选)**：如果你希望本地克隆仓库以便离线查阅或贡献内容。\n\n> **国内访问提示**：\n> 由于原始链接多指向 Google Scholar，国内用户直接点击可能无法打开。建议配合学术镜像站（如 [Google Scholar 镜像](https:\u002F\u002Fsc.panda321.com\u002F) 或学校图书馆数据库）使用，或在 GitHub 页面直接搜索论文标题进行下载。\n\n## 获取与浏览步骤\n\n### 方式一：在线浏览（推荐）\n最直接的方式是直接访问 GitHub 仓库页面，利用目录跳转查找所需领域的综述。\n\n1. 访问项目主页：\n   ```text\n   https:\u002F\u002Fgithub.com\u002Fmetrofun\u002Fmachine-learning-surveys\n   ```\n2. 在页面上方的 **Table of Contents** 中，点击你感兴趣的方向（如 `Deep Learning`, `Computer Vision`, `NLP` 等）。\n3. 页面会自动跳转至对应章节，查看推荐的论文标题、作者、年份及页数。\n4. 点击论文标题链接（通常指向 Google Scholar 搜索结果），复制标题到国内学术搜索引擎或知网\u002F万方进行下载。\n\n### 方式二：本地克隆（适合离线查阅或贡献）\n如果你希望将列表保存到本地，或打算参与贡献，可以使用 Git 克隆。\n\n1. 打开终端（Terminal 或 CMD）。\n2. 执行以下命令克隆仓库：\n   ```bash\n   git clone https:\u002F\u002Fgithub.com\u002Fmetrofun\u002Fmachine-learning-surveys.git\n   ```\n   *若速度较慢，可使用国内加速地址：*\n   ```bash\n   git clone https:\u002F\u002Fgitee.com\u002Fmirrors\u002Fmachine-learning-surveys.git\n   ```\n   *(注：若 Gitee 镜像不存在，请尝试使用 `git clone https:\u002F\u002Fgithub.com.cnpmjs.org\u002Fmetrofun\u002Fmachine-learning-surveys.git` 或其他 GitHub 加速服务)*\n\n3. 进入目录并查看内容：\n   ```bash\n   cd machine-learning-surveys\n   # 在支持的编辑器中打开 README.md，或使用命令行查看\n   cat README.md\n   ```\n\n## 基本使用示例\n\n假设你是一名开发者，想要快速了解 **深度学习 (Deep Learning)** 领域的经典综述。\n\n**步骤 1：定位章节**\n在仓库的 `README.md` 中找到 `### Deep Learning` 部分。\n\n**步骤 2：筛选资源**\n你会看到如下条目（示例）：\n- **[Deep Learning](...)** (2016) by [IJ Goodfellow, Y Bengio, A Courville] [800pp] 📚 ⭐⭐\n  - *说明：这是著名的“花书”，深度学习领域的圣经级教材。*\n- **[Deep Learning in Neural Networks: An Overview](...)** (2015) by [J Schmidhuber] [88pp] ⭐⭐\n  - *说明：由深度学习先驱 Jürgen Schmidhuber 撰写的概述。*\n\n**步骤 3：获取文献**\n由于直接链接可能受限，请复制论文标题 `\"Deep Learning\"` 和作者 `\"Goodfellow\"`，在以下平台搜索下载：\n- 国内：知网 (CNKI)、万方数据、百度学术\n- 国际镜像：arXiv, Semantic Scholar, 或图书馆资源\n\n**步骤 4：进阶利用（贡献列表）**\n如果你发现了一篇优秀的综述未被收录，可以按照以下方式贡献：\n1. 查阅项目的 `How to Contribute` 维基页面。\n2. 按照格式在本地 `README.md` 中添加新条目。\n3. 提交 Pull Request (PR)。\n\n---\n*提示：列表中的 📚 图标代表书籍，⭐ 数量代表推荐程度。*","某生物科技公司算法团队正启动“深度学习在基因测序数据中的应用”预研项目，急需快速掌握该交叉领域的最新进展与核心难点。\n\n### 没有 machine-learning-surveys 时\n- **检索效率极低**：研究人员需在 Google Scholar、arXiv 等多个平台反复搜索\"Bioinformatics\"、\"Deep Learning\"等关键词，耗费数天筛选低质量论文。\n- **缺乏系统视野**：只能零散阅读单篇技术文章，难以厘清从传统生物信息学到深度学习的演进脉络，容易陷入局部细节而忽略整体架构。\n- **经典文献遗漏**：因不熟悉领域历史，极易错过如 T Ching 等人关于“生物学中深度学习机遇与障碍”的关键综述，导致重复造轮子或方向偏差。\n- **入门门槛过高**：新入职的计算机背景成员缺乏针对性的跨学科教材指引（如 A Lesk 的经典著作），上手周期被大幅拉长。\n\n### 使用 machine-learning-surveys 后\n- **资源一键直达**：直接查阅\"Bioinformatics\"分类，瞬间获取按时间排序的高质量综述列表，将文献调研时间从数天压缩至几小时。\n- **知识体系清晰**：通过阅读列表中精选的长篇综述（如 102 页的深度分析），团队迅速构建起完整的领域知识地图，明确了技术边界。\n- **精准锁定经典**：借助社区 curated 机制，直接定位到高星标记的权威文献，确保研究起点建立在坚实的理论基础之上。\n- **学习路径明确**：新成员依据列表中的教程与书籍推荐（如面向计算机科学家的生物信息学导论），实现了跨领域知识的快速补齐。\n\nmachine-learning-surveys 的核心价值在于将分散杂乱的学术海洋转化为结构化的知识导航图，让研发团队从“找资料”转向“用知识”。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmetrofun_machine-learning-surveys_8e6aa2b7.png","metrofun","Dmytrii S.","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fmetrofun_95a7931b.png",null,"Daimler*\u002FAmazon\u002FNokia\u002FYandex","Berlin","http:\u002F\u002Fmlreview.com","https:\u002F\u002Fgithub.com\u002Fmetrofun",[84],{"name":85,"color":86,"percentage":87},"JavaScript","#f1e05a",100,1392,198,"2026-03-30T00:06:51",1,"","未说明",{"notes":95,"python":93,"dependencies":96},"该项目并非可执行的软件工具或代码库，而是一个机器学习和相关领域的综述、概述及书籍的精选列表（Curated List）。README 内容仅包含指向学术论文和书籍的链接，不涉及任何代码运行、环境配置、依赖安装或硬件资源需求。用户只需通过浏览器访问链接阅读文献即可。",[],[13],[99,100,101,102],"machine-learning","awesome","awesome-list","list","2026-03-27T02:49:30.150509","2026-04-06T08:18:29.445746",[],[]]