[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-dustinvtran--ml-videos":3,"tool-dustinvtran--ml-videos":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",160784,2,"2026-04-19T11:32:54",[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":76,"owner_twitter":73,"owner_website":79,"owner_url":80,"languages":76,"stars":81,"forks":82,"last_commit_at":83,"license":76,"difficulty_score":84,"env_os":85,"env_gpu":86,"env_ram":86,"env_deps":87,"category_tags":90,"github_topics":92,"view_count":32,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":99,"updated_at":100,"faqs":101,"releases":102},9595,"dustinvtran\u002Fml-videos","ml-videos","A collection of video resources for machine learning","ml-videos 是一个专注于机器学习领域的视频资源聚合库，旨在为学习者和从业者提供一站式的学术视频导航。它系统性地整理了来自全球顶级会议（如 NeurIPS、ICML、CVPR、ICLR）、研讨会、暑期学校及各类 seminars 的演讲录像，涵盖了从基础数学概率概述到前沿生物医学研究应用的广泛内容。\n\n在机器学习知识更新极快且分散的背景下，ml-videos 有效解决了优质学术视频资源难以查找和整合的痛点。用户无需在 YouTube、SlidesLive、VideoLectures.NET 等多个平台间反复搜索，即可通过该清单快速定位特定年份或特定会议的高质量讲座与论文解读视频。\n\n该项目非常适合机器学习研究人员、数据科学家、高校学生以及希望深入理解算法原理的开发者使用。无论是需要追踪最新科研动态的专家，还是希望通过 Nando de Freitas 或 Alex Smola 等名家课程打基础的学习者，都能从中获益。其独特的亮点在于不仅收录了近年来的会议实录，还保留了经典的通用教学资源链接，并采用社区协作模式持续维护，确保资源库的时效性与丰富度，是构建个人机器学习知识体系的得力助","ml-videos 是一个专注于机器学习领域的视频资源聚合库，旨在为学习者和从业者提供一站式的学术视频导航。它系统性地整理了来自全球顶级会议（如 NeurIPS、ICML、CVPR、ICLR）、研讨会、暑期学校及各类 seminars 的演讲录像，涵盖了从基础数学概率概述到前沿生物医学研究应用的广泛内容。\n\n在机器学习知识更新极快且分散的背景下，ml-videos 有效解决了优质学术视频资源难以查找和整合的痛点。用户无需在 YouTube、SlidesLive、VideoLectures.NET 等多个平台间反复搜索，即可通过该清单快速定位特定年份或特定会议的高质量讲座与论文解读视频。\n\n该项目非常适合机器学习研究人员、数据科学家、高校学生以及希望深入理解算法原理的开发者使用。无论是需要追踪最新科研动态的专家，还是希望通过 Nando de Freitas 或 Alex Smola 等名家课程打基础的学习者，都能从中获益。其独特的亮点在于不仅收录了近年来的会议实录，还保留了经典的通用教学资源链接，并采用社区协作模式持续维护，确保资源库的时效性与丰富度，是构建个人机器学习知识体系的得力助手。","# Machine Learning Videos\n\nThis is a collection of recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous programs. It is actively maintained; please add to this list by submitting a pull request!\n\nThis collection builds off the original compilation from [a blog post](http:\u002F\u002Fdustintran.com\u002Fblog\u002Fvideo-resources-for-machine-learning).\n\n## General\n\nIn general the following are excellent resources:\n\n+ [VideoLectures.NET](http:\u002F\u002Fvideolectures.net\u002F) is the primary video archive for machine learning. Much of its content focuses on the field, incorporating conferences, workshops, lectures, and even discussions.\n+ [TechTalks.tv](http:\u002F\u002Ftechtalks.tv\u002F) is the second most used archive. Notably, many ICML videos are located here.\n+ [Slideslive](https:\u002F\u002Fslideslive.com\u002F) is recently being used by the ML community. Eg: ICML, NeurIPS, ICLR.\n+ [Youtube](http:\u002F\u002Fyoutube.com\u002F) contains a few, such as certain AISTATS and ICLR years. It's best for collecting lectures, such as by [Nando de Freitas](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) and [Alex Smola](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn). Also, the user [mathematicalmonk](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fmathematicalmonk) has created several basic mathematical and probability overviews for many [ML methods](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD0F06AA0D2E8FFBA).\n+ [Models, Inference, & Algorithms](https:\u002F\u002Fwww.broadinstitute.org\u002Fscientific-community\u002Fscience\u002Fmia\u002Fmodels-inference-algorithms) at the Broad Institute of MIT and Harvard has slide decks and a growing [playlist](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlMMtlgw6qNjROoMNTBQjAcdx53kV50cS) on machine learning for biomedical research.\n\n## 2020\n\n+ [Conference on Learning Theory](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCHBdlWR7RYw4LAGsUrCLamYRnBf9pTTT)\n+ [Conference on Computer Vision and Pattern Recognition - Papers, Workshops, Tutorials](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fplaylists)\n+ [International Conference on Machine Learning](https:\u002F\u002Fslideslive.com\u002Ficml-2020)\n+ International Conference on Learning Representations ([Keynote+Q&A](https:\u002F\u002Ficlr.cc\u002Fvirtual_2020\u002Fcalendar.html)) ([Papers](https:\u002F\u002Ficlr.cc\u002Fvirtual_2020\u002Fpapers.html?filter=keywords\u002F))\n+ [AAAI Conference on Artificial Intelligence](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-20\u002Flivestreamed-talks\u002F)\n\n## 2019\n\n+ [Neural Information Processing Systems - Papers, Tutorials and Workshops](https:\u002F\u002Fslideslive.com\u002Fneurips)\n+ [Cognitive Computational Neuroscience](https:\u002F\u002Fccneuro.org\u002F2019\u002Fvideos.asp)\n+ [Annual Conference of the North American Chapter of the Association for Computational Linguistics](https:\u002F\u002Fvimeo.com\u002Fchannels\u002F1503729)\n+ [International Joint Conference on Artificial Intelligence](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Fijcai\u002Fvideos)\n+ [Conference on Learning Theory](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCHBdlWR7RYz4jx6NPdnI_1rP3kj0E3G8)\n+ International Conference on Computer Vision ([Oral Session](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fvideos))\n+ Conference on Computer Vision and Pattern Recognition ([Oral Session](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fvideos))\n+ [International Conference on Machine Learning](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficml.imls\u002Fvideos\u002F), alternate link ([[1]](https:\u002F\u002Fslideslive.com\u002Ficml))\n+ [Learning for Dynamics and Control](https:\u002F\u002Fl4dc.mit.edu\u002Fvideos\u002F)\n+ [International Conference on Learning Representations](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficlr.cc\u002Fvideos\u002F?ref=page_internal) ([Workshops](https:\u002F\u002Fslideslive.com\u002Ficlr\u002F))\n+ [AAAI Conference on Artificial Intelligence](https:\u002F\u002Fvideos.videoken.com\u002Findex.php\u002Fvideoscategory\u002Faaai-2019\u002F)\n\n## 2018\n\n+ [Cognitive Computational Neuroscience](https:\u002F\u002Fccneuro.org\u002F2018\u002Fvideos.asp)\n+ [Empirical Methods on Natural Language Processing](https:\u002F\u002Fvimeo.com\u002Fchannels\u002F1427394)\n+ [Annual Meeting of the Association for Computational Linguistics](https:\u002F\u002Fvimeo.com\u002Fshowcase\u002F5391494)\n+ [Neural Information Processing Systems Workshop, Security in Machine Learning](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLFG9vaKTeJq4IpOje38YWA9UQu_COeNve)\n+ [Neural Information Processing Systems - Tutorials, Spotlights and Posters](https:\u002F\u002Fnips.cc\u002FConferences\u002F2018\u002FVideos)\n+ [International Conference on Probabilistic Programming](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_PW0E_Tf2qvXBEpl10Y39RULTN-ExzZQ)\n+ [Deep|Bayes – Summer school on Deep Learning and Bayesian Methods, Moscow](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62)\n+ [Deep Learning Summer School and Reinforcement Learning Summer School, Toronto](http:\u002F\u002Fvideolectures.net\u002FDLRLsummerschool2018_toronto\u002F)\n+ [Conference on Learning Theory](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCHBdlWR7RYzMjoGTatLItEhisi5sBNFh)\n+ [Montreal AI Symposium](http:\u002F\u002Fmontrealaisymposium.com\u002F)\n+ [AAAI Conference on Artificial Intelligence](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_9a5ic6GUim0HB71cILHmQwfdKiwZ-MG)\n+ [Annual Meeting of the Cognitive Science Society](https:\u002F\u002Fcogsci.tv\u002Fcategory\u002Fcogsci2018\u002F) (pswd:cogscitv)\n+ [Annual Conference of the North American Chapter of the Association for Computational Linguistics](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Fnaacl2018\u002Fvideos\u002F)\n+ [International Joint Conference on Artificial Intelligence](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Fijcaiconf\u002Fvideos)\n+ [International Conference on Machine Learning](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficml.imls\u002Fvideos\u002F)\n+ [Conference on Computer Vision and Pattern Recognition](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_bDvITUYucCIT8iNGW8zCXeY5_u6hg-y) ([Tutorials](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_bDvITUYucD54Ym5XKGqTv9xNsrOX0aS), [Workshops](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_bDvITUYucB5y7d9KbROEtrbLrE4IuR7))\n+ [International Conference on Learning Representations](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficlr.cc\u002Fposts\u002F)\n+ [Data, Learning, and Inference](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-tWvTpyd1VD0d5lXQS53QlQm0fxOcC17), including workshops ([[1]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=D6uqGwBGMgc&list=PL-tWvTpyd1VCXhZK7oIcb74-MvrH9Ea3c), [[2]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2vxZbZC21Gg&list=PL-tWvTpyd1VAlbzhCpljlREd76Nlo1pOo))\n+ [TensorFlow Dev Summit](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQY2H8rRoyvxjVx3zfw4vA4cvlKogyLNN)\n+ [Scaled Machine Learning](https:\u002F\u002Fwww.matroid.com\u002Fblog\u002Fpost\u002Fslides-and-videos-from-scaledml-2018)\n+ [SysML Conference](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUChutDKIa-AYyAmbT45s991g\u002Fvideos?disable_polymer=1)\n\n## 2017\n\n+ [Annual Meeting of the Association for Computational Linguistics](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Facl2017)\n+ Montreal AI Symposium [[Morning Session]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=02xIkHowQOk) [[Afternoon Session]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cdcKwefTT6M)\n+ [AAAI Conference on Artificial Intelligence](http:\u002F\u002Fvideolectures.net\u002Faaai2017_sanfrancisco\u002F)\n+ [Annual Meeting of the Cognitive Science Society](https:\u002F\u002Fcogsci.tv\u002Fcategory\u002Fcogsci2017\u002F) (pswd:cogscitv)\n+ [AI By the Bay](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLNESult6cnOk3Q8tjfSIWy49Fz37l0wZU) \n+ [International Conference on Machine Learning - Tutorials, Invited Talks and Papers](https:\u002F\u002Ficml.cc\u002FConferences\u002F2017\u002FVideos)\n+ [Neural Information Processing Systems Workshop, Deep Learning for Physical Sciences](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6_fD5q0zQxshmFJCSBaA5Jglf62Ct4Vm)\n+ [Neural Information Processing Systems Workshop, Approximate Inference](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLsatQfvo0v3sUhi3ijRme9MyqwLL5EOiG)\n+ [Machine Learning Summer School, Tübingen, Germany](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9)\n+ [Speech and Audio in the Northeast](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg)\n+ [Neural Information Processing Systems - Tutorials, Invited Talks, Papers and Symposiums](https:\u002F\u002Fnips.cc\u002FConferences\u002F2017\u002FVideos)\n+ [International Conference on Learning Representations](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficlr.cc\u002Fvideos\u002F?ref=page_internal)\n+ [Deep Learning: Theory, Algorithms, and Applications](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW)\n+ [Deep Reinforcement Learning Bootcamp](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdeep-rl-bootcamp\u002Flectures)\n+ [Cognitive Computational Neuroscience](http:\u002F\u002Fccneuro.org\u002F2017videos\u002F)\n+ [Gaussian Process Summer School, Sheffield](http:\u002F\u002Fgpss.cc\u002Fgpss17\u002Fprogram)\n+ [Empirical Methods on Natural Language Processing](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Femnlp2017)\n+ [Deep Learning Summer School and Reinforcement Learning Summer School, Montreal](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2017_montreal\u002F)\n+ [Computer Vision and Pattern Recognition - Spotlights, Orals and Tutorials](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fplaylists)\n+ [GPU Technology Conference](http:\u002F\u002Fon-demand-gtc.gputechconf.com\u002Fgtc-quicklink\u002Fg6VqzTi)\n+ [Data, Learning, and Inference Workshop, Data Efficient Reinforcement Learning](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8)\n+ [Edinburgh Deep Learning Workshop](http:\u002F\u002Fworkshops.inf.ed.ac.uk\u002Fdeep\u002Fdeep2017\u002F)\n+ [Workshop on Validating and Expanding Approximate Bayesian Computation Methods](http:\u002F\u002Fwww.birs.ca\u002Fevents\u002F2017\u002F5-day-workshops\u002F17w5025\u002Fvideos)\n+ [TensorFlow Dev Summit](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLOU2XLYxmsIKGc_NBoIhTn2Qhraji53cv)\n+ [Foundations of Machine Learning Boot Camp, Simons Institute](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F3748)\n+ [StanCon](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DJ0c7Bm5Djk)\n\n## 2016\n\n+ [Empirical Methods on Natural Language Processing](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Femnlp2016)\n+ [AAAI Conference on Artificial Intelligence](http:\u002F\u002Fvideolectures.net\u002Faaai2016_phoenix\u002F)\n+ [European Conference on Computer Vision](http:\u002F\u002Fvideolectures.net\u002Feccv2016_amsterdam\u002F)\n+ [Computer Vision and Pattern Recognition](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fplaylists)\n+ [Neural Information Processing Systems Workshop, Nonconvex Optimization: Theory and Practice](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSkaTb2vk5tATPJB_VTgguwmS89GVznN0)\n+ [Neural Information Processing Systems Symposium, RNN and Other Machines That Learn Algorithms](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLPwzH56Rdmq4hcuEMtvBGxUrcQ4cAkoSc)\n+ [Neural Information Processing Systems Workshop, Bayesian Deep Learning](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrcr1zcpLZb7paw9LNgx-zsCr-PstjBM6)\n+ [Neural Information Processing Systems Workshop, Reliable Machine Learning in the Wild](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fwildml2016nips\u002Fschedule)\n+ [Neural Information Processing Systems Workshop, Approximate Inference](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8Yb49e5zFuztzY4wZRp_XIj6PREg3pw8)\n+ [Neural Information Processing Systems Workshop, Adversarial Training](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJscN9YDD1buxCitmej1pjJkR5PMhenTF)\n+ [Neural Information Processing Systems](https:\u002F\u002Fnips.cc\u002FConferences\u002F2016\u002FSchedule)\n+ [Bay Area Deep Learning School](https:\u002F\u002Fwww.bayareadlschool.org\u002F)\n+ [Deep Learning, Tools and Methods Workshop](https:\u002F\u002Fportal.klewel.com\u002Fwatch\u002Fwebcast\u002Fdeep-learning-tools-and-methods-workshop\u002F)\n+ [Knowledge Discovery and Data Mining](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCPsUUDUlcTJuP-fRa7z85aQ\u002Fplaylists)\n+ [Deep Learning Summer School, Montreal](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2016_montreal\u002F)\n+ [Uncertainty in Computation, Simons Institute](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Flogic2016-1)\n+ [Machine Learning Summer School, Peru](http:\u002F\u002Fwww.ucsp.edu.pe\u002Fciet\u002Fmlss16\u002Fspeakers.html)\n+ [International Conference on Machine Learning](http:\u002F\u002Ftechtalks.tv\u002Ficml\u002F2016\u002F)\n+ [Machine Learning Summer School, Cadiz](http:\u002F\u002Flearning.mpi-sws.org\u002Fmlss2016\u002Fvideos\u002F)\n+ [International Conference on Learning Representations](http:\u002F\u002Fvideolectures.net\u002Ficlr2016_san_juan\u002F)\n\n## 2015\n\n+ [Empirical Methods on Natural Language Processing](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Femnlp2015)\n+ [Computer Vision and Pattern Recognition](http:\u002F\u002Ftechtalks.tv\u002Fcvpr\u002F2015\u002F)\n+ [International Conference on Computer Vision](http:\u002F\u002Fvideolectures.net\u002Ficcv2015_santiago\u002F)\n+ [Neural Information Processing Systems](https:\u002F\u002Fnips.cc\u002FConferences\u002F2015\u002FSchedule)\n+ [Gaussian Process Summer School, Sheffield](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhoHEZlJjdQJLRUSE9_55eXkwNXKTiNQF)\n+ [Deep Learning Summer School, Montreal](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2015_montreal\u002F)\n+ [International Conference on Machine Learning](http:\u002F\u002Fvideolectures.net\u002Ficml2015_lille\u002F)\n+ [Uncertainty in Artificial Intelligence](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCXDf7Y4KMcqPWHriorcMDNg\u002Fvideos)\n+ [Arthur M. Sackler Colloquia: Drawing Causal Inference from Big Data](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLGJm1x3XQeK0NgFOX2Z7Wt-P5RU5Zv0Hv)\n+ [International Conference on Learning Representations](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhiWXaTdsWB8PnrVZquVyqlRFWXM4ijYz)\n+ [Conference on Learning Theory](http:\u002F\u002Fvideolectures.net\u002Fcolt2015_paris\u002F)\n+ [Machine Learning Summer School, Tübingen](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2015\u002Fspeakers.html)\n+ [Machine Learning Summer School, Sydney](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCT1k2e63pqm_VSXmaF21n6g\u002Fvideos)\n\n## 2014\n\n+ [Computer Vision and Pattern Recognition - Oral](http:\u002F\u002Ftechtalks.tv\u002Fcvpr-2014-oral-talks\u002F)\n+ [Computer Vision and Pattern Recognition - Spotlights](http:\u002F\u002Ftechtalks.tv\u002Fcvpr-2014-video-highlights\u002F)\n+ [European Conference on Computer Vision](http:\u002F\u002Fvideolectures.net\u002Feccv2014_zurich\u002F)\n+ [UToronto Workshop on Big Data and Statistical Machine Learning](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F316\u002F2014)\n+ [Neural Information Processing Systems](https:\u002F\u002Fnips.cc\u002FConferences\u002F2014\u002FSchedule)\n+ [Gaussian Process Summer School, Sheffield](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhoHEZlJjdQKgrpK70Ym04Ju3-9W-QXns)\n+ [Machine Learning Summer School, Pittsburgh](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz)\n+ [International Conference on Machine Learning](http:\u002F\u002Ftechtalks.tv\u002Ficml2014\u002F)\n+ [Conference on Learning Theory](http:\u002F\u002Fvideolectures.net\u002Fcolt2014_barcelona\u002F)\n+ [Artificial Intelligence and Statistics](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCQeS3L6d-S6ZeCQChFyK5Uw)\n\n## 2013\n\n+ [International Conference on Computer Vision](http:\u002F\u002Ftechtalks.tv\u002Ficcv2013\u002F)\n+ [Gaussian Process Winter School, Sheffield](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhoHEZlJjdQKI1cs5yPRUYdgcsE0HctoQ)\n+ [Machine Learning Summer School, Iceland](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC3ywjSv5OsDiDAnOP8C1NiQ)\n+ [Neural Information Processing Systems](https:\u002F\u002Fnips.cc\u002FConferences\u002F2013\u002FSchedule)\n+ [Neural Information Processing Systems Workshops](http:\u002F\u002Fvideolectures.net\u002Fnipsworkshops2013_laketahoe\u002F)\n+ [International Conference on Machine Learning](http:\u002F\u002Ftechtalks.tv\u002Ficml2013\u002F)\n+ [Machine Learning Summer School, Tübingen](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E)\n+ [Conference on Learning Theory](http:\u002F\u002Fvideolectures.net\u002Fcolt2013_princeton\u002F)\n+ [Harvard Institute for Quantitative Social Science, Applied Statistics Workshop](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLoKvRqQVbtI-ZhBpBrKT2KllP5dH8O0M)\n\n## 2012\n\n+ [Brown ICERM workshops](https:\u002F\u002Ficerm.brown.edu\u002Fvideo_archive\u002F) (the [2012 Bayesian nonparametrics](https:\u002F\u002Ficerm.brown.edu\u002Fsp-f12-w1\u002F) is particularly good)\n+ [Neural Information Processing Systems](http:\u002F\u002Fvideolectures.net\u002Fnips2012_laketahoe\u002F)\n+ [Uncertainty in Artificial Intelligence](http:\u002F\u002Fvideolectures.net\u002Fuai2012_catalinaislands\u002F)\n+ [International Conference on Machine Learning](http:\u002F\u002Ftechtalks.tv\u002Fsearch\u002Fresults\u002F?q=icml+2012)\n+ [AAAI Conference on Artificial Intelligence](http:\u002F\u002Fvideolectures.net\u002Faaai2012_toronto\u002F)\n\n## 2011\n\n+ [Neural Information Processing Systems](http:\u002F\u002Fvideolectures.net\u002Fnips2011_granada\u002F)\n+ [Neural Information Processing Systems Workshops](http:\u002F\u002Fvideolectures.net\u002Fnipsworkshops2011_sierranevada\u002F)\n+ [Uncertainty in Artificial Intelligence](http:\u002F\u002Fvideolectures.net\u002Fuai2011_barcelona\u002F)\n+ [Conference on Learning Theory](http:\u002F\u002Fvideolectures.net\u002Fcolt2011_budapest\u002F)\n+ [Artificial Intelligence and Statistics](http:\u002F\u002Fvideolectures.net\u002Faistats2011_fortlauderdale\u002F)\n\n## 2010\n\n+ [Neural Information Processing Systems](http:\u002F\u002Fvideolectures.net\u002Fnips2010_vancouver\u002F)\n+ [International Conference on Machine Learning](http:\u002F\u002Fvideolectures.net\u002Ficml2010_haifa\u002F)\n+ [Artificial Intelligence and Statistics](http:\u002F\u002Fvideolectures.net\u002Faistats2010_sardinia\u002F)\n\n## 2009\n\n+ [Neural Information Processing Systems](http:\u002F\u002Fvideolectures.net\u002Fnips09_vancouver\u002F)\n+ [Machine Learning Summer School, Cambridge](http:\u002F\u002Fvideolectures.net\u002Fmlss09uk_cambridge\u002F)\n+ [International Conference on Machine Learning](http:\u002F\u002Fvideolectures.net\u002Ficml09_montreal\u002F)\n\n## 2008\n\n+ [Knowledge Discovery and Data Mining](http:\u002F\u002Fvideolectures.net\u002Fkdd08_las_vegas\u002F)\n+ [Uncertainty in Artificial Intelligence](http:\u002F\u002Fvideolectures.net\u002Fuai08_helsinki\u002F)\n+ [International Conference on Machine Learning](http:\u002F\u002Fvideolectures.net\u002Ficml08_helsinki\u002F)\n\n## 2007\n\n+ [International Conference on Machine Learning](http:\u002F\u002Fvideolectures.net\u002Ficml07_corvallis\u002F)\n\n## 2006\n\n+ [Neural Information Processing Systems](http:\u002F\u002Fvideolectures.net\u002Fnips06_whistler\u002F)\n\n## 2005\n\n+ [Neural Information Processing Systems Workshop: Kernels](http:\u002F\u002Fvideolectures.net\u002Fnips05_whistler\u002F)\n+ [International Conference on Machine Learning](http:\u002F\u002Fvideolectures.net\u002Ficml05_bonn\u002F)\n","# 机器学习视频\n\n这是一个收录了机器学习会议、研讨会、讲座、暑期学校及其他各类活动录像的合集。该合集会持续更新，请通过提交 Pull Request 来添加内容！\n\n本合集基于 [博客文章](http:\u002F\u002Fdustintran.com\u002Fblog\u002Fvideo-resources-for-machine-learning) 中的原始整理。\n\n## 总览\n\n总体而言，以下资源非常优秀：\n\n+ [VideoLectures.NET](http:\u002F\u002Fvideolectures.net\u002F) 是机器学习领域的主要视频档案库。其内容以机器学习为核心，涵盖会议、研讨会、讲座，甚至讨论环节。\n+ [TechTalks.tv](http:\u002F\u002Ftechtalks.tv\u002F) 是使用第二广泛的档案库。值得注意的是，许多 ICML 的视频都存放于此。\n+ [Slideslive](https:\u002F\u002Fslideslive.com\u002F) 近来被机器学习社区广泛采用。例如：ICML、NeurIPS、ICLR 等。\n+ [Youtube](http:\u002F\u002Fyoutube.com\u002F) 也收录了一些视频，比如某些 AISTATS 和 ICLR 年度会议。它尤其适合收集讲座，例如由 [Nando de Freitas](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) 和 [Alex Smola](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) 主讲的内容。此外，用户 [mathematicalmonk](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fmathematicalmonk) 还为多种 [ML 方法](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD0F06AA0D2E8FFBA) 制作了基础的数学和概率概述视频。\n+ 哈佛-麻省理工学院布罗德研究所的 [Models, Inference, & Algorithms](https:\u002F\u002Fwww.broadinstitute.org\u002Fscientific-community\u002Fscience\u002Fmia\u002Fmodels-inference-algorithms) 提供了幻灯片资料，并拥有一个不断增长的 [播放列表](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLlMMtlgw6qNjROoMNTBQjAcdx53kV50cS)，专注于生物医学研究中的机器学习。\n\n## 2020 年\n\n+ [学习理论会议](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCHBdlWR7RYw4LAGsUrCLamYRnBf9pTTT)\n+ [计算机视觉与模式识别会议 - 论文、研讨会、教程](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fplaylists)\n+ [国际机器学习会议](https:\u002F\u002Fslideslive.com\u002Ficml-2020)\n+ 国际学习表征会议（[主题演讲+问答](https:\u002F\u002Ficlr.cc\u002Fvirtual_2020\u002Fcalendar.html)）（[论文](https:\u002F\u002Ficlr.cc\u002Fvirtual_2020\u002Fpapers.html?filter=keywords\u002F))\n+ [AAAI 人工智能会议](https:\u002F\u002Faaai.org\u002FConferences\u002FAAAI-20\u002Flivestreamed-talks\u002F)\n\n## 2019 年\n\n+ [神经信息处理系统 - 论文、教程和研讨会](https:\u002F\u002Fslideslive.com\u002Fneurips)\n+ [认知计算神经科学](https:\u002F\u002Fccneuro.org\u002F2019\u002Fvideos.asp)\n+ [北美计算语言学协会年会](https:\u002F\u002Fvimeo.com\u002Fchannels\u002F1503729)\n+ [国际人工智能联合会议](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Fijcai\u002Fvideos)\n+ [学习理论会议](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCHBdlWR7RYz4jx6NPdnI_1rP3kj0E3G8)\n+ 国际计算机视觉会议（[口头报告环节](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fvideos)）\n+ 计算机视觉与模式识别会议（[口头报告环节](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fvideos)）\n+ [国际机器学习会议](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficml.imls\u002Fvideos\u002F)，备用链接（[[1]](https:\u002F\u002Fslideslive.com\u002Ficml)）\n+ [动力学与控制学习](https:\u002F\u002Fl4dc.mit.edu\u002Fvideos\u002F)\n+ [国际学习表征会议](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficlr.cc\u002Fvideos\u002F?ref=page_internal)（[研讨会](https:\u002F\u002Fslideslive.com\u002Ficlr\u002F)）\n+ [AAAI 人工智能会议](https:\u002F\u002Fvideos.videoken.com\u002Findex.php\u002Fvideoscategory\u002Faaai-2019\u002F)\n\n## 2018 年\n\n+ [认知计算神经科学](https:\u002F\u002Fccneuro.org\u002F2018\u002Fvideos.asp)\n+ [自然语言处理经验方法](https:\u002F\u002Fvimeo.com\u002Fchannels\u002F1427394)\n+ [计算语言学协会年会](https:\u002F\u002Fvimeo.com\u002Fshowcase\u002F5391494)\n+ [神经信息处理系统研讨会：机器学习中的安全性](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLFG9vaKTeJq4IpOje38YWA9UQu_COeNve)\n+ [神经信息处理系统 - 教程、亮点与海报](https:\u002F\u002Fnips.cc\u002FConferences\u002F2018\u002FVideos)\n+ [国际概率编程会议](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_PW0E_Tf2qvXBEpl10Y39RULTN-ExzZQ)\n+ [Deep|Bayes – 莫斯科深度学习与贝叶斯方法暑期学校](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLe5rNUydzV9Q01vWCP9BV7NhJG3j7mz62)\n+ [多伦多深度学习暑期学校和强化学习暑期学校](http:\u002F\u002Fvideolectures.net\u002FDLRLsummerschool2018_toronto\u002F)\n+ [学习理论会议](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLCHBdlWR7RYzMjoGTatLItEhisi5sBNFh)\n+ [蒙特利尔人工智能研讨会](http:\u002F\u002Fmontrealaisymposium.com\u002F)\n+ [AAAI 人工智能会议](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_9a5ic6GUim0HB71cILHmQwfdKiwZ-MG)\n+ [认知科学学会年会](https:\u002F\u002Fcogsci.tv\u002Fcategory\u002Fcogsci2018\u002F)（密码：cogscitv）\n+ [北美计算语言学协会年会](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Fnaacl2018\u002Fvideos\u002F)\n+ [国际人工智能联合会议](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Fijcaiconf\u002Fvideos)\n+ [国际机器学习会议](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficml.imls\u002Fvideos\u002F)\n+ [计算机视觉与模式识别会议](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_bDvITUYucCIT8iNGW8zCXeY5_u6hg-y)（[教程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_bDvITUYucD54Ym5XKGqTv9xNsrOX0aS)、[研讨会](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL_bDvITUYucB5y7d9KbROEtrbLrE4IuR7)）\n+ [国际学习表征会议](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficlr.cc\u002Fposts\u002F)\n+ [数据、学习与推断](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-tWvTpyd1VD0d5lXQS53QlQm0fxOcC17)，包括研讨会（[[1]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=D6uqGwBGMgc&list=PL-tWvTpyd1VCXhZK7oIcb74-MvrH9Ea3c)、[[2]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2vxZbZC21Gg&list=PL-tWvTpyd1VAlbzhCpljlREd76Nlo1pOo))\n+ [TensorFlow 开发者峰会](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLQY2H8rRoyvxjVx3zfw4vA4cvlKogyLNN)\n+ [规模化机器学习](https:\u002F\u002Fwww.matroid.com\u002Fblog\u002Fpost\u002Fslides-and-videos-from-scaledml-2018)\n+ [SysML 会议](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUChutDKIa-AYyAmbT45s991g\u002Fvideos?disable_polymer=1)\n\n## 2017年\n\n+ [计算语言学协会年会](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Facl2017)\n+ 蒙特利尔人工智能研讨会 [[上午场]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=02xIkHowQOk) [[下午场]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cdcKwefTT6M)\n+ [AAAI人工智能会议](http:\u002F\u002Fvideolectures.net\u002Faaai2017_sanfrancisco\u002F)\n+ [认知科学学会年会](https:\u002F\u002Fcogsci.tv\u002Fcategory\u002Fcogsci2017\u002F) (密码:cogscitv)\n+ [湾区人工智能大会](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLNESult6cnOk3Q8tjfSIWy49Fz37l0wZU) \n+ [国际机器学习大会 - 教程、特邀报告和论文](https:\u002F\u002Ficml.cc\u002FConferences\u002F2017\u002FVideos)\n+ [神经信息处理系统研讨会，物理科学中的深度学习](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL6_fD5q0zQxshmFJCSBaA5Jglf62Ct4Vm)\n+ [神经信息处理系统研讨会，近似推断](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLsatQfvo0v3sUhi3ijRme9MyqwLL5EOiG)\n+ [德国图宾根机器学习暑期学校](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFUOvoYCdKikfck8YeUCnl9)\n+ [东北地区的语音与音频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBJWRPcgwk7tNLaBVu_S90ZQSblO3bwjg)\n+ [神经信息处理系统 - 教程、特邀报告、论文和研讨会](https:\u002F\u002Fnips.cc\u002FConferences\u002F2017\u002FVideos)\n+ [国际表征学习大会](https:\u002F\u002Fwww.facebook.com\u002Fpg\u002Ficlr.cc\u002Fvideos\u002F?ref=page_internal)\n+ [深度学习：理论、算法与应用](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW)\n+ [深度强化学习训练营](https:\u002F\u002Fsites.google.com\u002Fview\u002Fdeep-rl-bootcamp\u002Flectures)\n+ [认知计算神经科学](http:\u002F\u002Fccneuro.org\u002F2017videos\u002F)\n+ [谢菲尔德高斯过程暑期学校](http:\u002F\u002Fgpss.cc\u002Fgpss17\u002Fprogram)\n+ [自然语言处理的实证方法](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Femnlp2017)\n+ [蒙特利尔深度学习与强化学习暑期学校](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2017_montreal\u002F)\n+ [计算机视觉与模式识别 - 焦点展示、口头报告和教程](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fplaylists)\n+ [GPU技术大会](http:\u002F\u002Fon-demand-gtc.gputechconf.com\u002Fgtc-quicklink\u002Fg6VqzTi)\n+ [数据、学习与推断研讨会，数据高效的强化学习](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL-tWvTpyd1VAvDpxukup6w-SuZQQ7e8K8)\n+ [爱丁堡深度学习研讨会](http:\u002F\u002Fworkshops.inf.ed.ac.uk\u002Fdeep\u002Fdeep2017\u002F)\n+ [验证与扩展近似贝叶斯计算方法研讨会](http:\u002F\u002Fwww.birs.ca\u002Fevents\u002F2017\u002F5-day-workshops\u002F17w5025\u002Fvideos)\n+ [TensorFlow开发者峰会](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLOU2XLYxmsIKGc_NBoIhTn2Qhraji53cv)\n+ [西蒙斯研究所机器学习基础训练营](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Fschedule\u002F3748)\n+ [StanCon](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DJ0c7Bm5Djk)\n\n## 2016年\n\n+ [自然语言处理的实证方法](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Femnlp2016)\n+ [AAAI人工智能会议](http:\u002F\u002Fvideolectures.net\u002Faaai2016_phoenix\u002F)\n+ [欧洲计算机视觉大会](http:\u002F\u002Fvideolectures.net\u002Feccv2016_amsterdam\u002F)\n+ [计算机视觉与模式识别](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fplaylists)\n+ [神经信息处理系统研讨会，非凸优化：理论与实践](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSkaTb2vk5tATPJB_VTgguwmS89GVznN0)\n+ [神经信息处理系统研讨会，RNN及其他学习算法的机器](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLPwzH56Rdmq4hcuEMtvBGxUrcQ4cAkoSc)\n+ [神经信息处理系统研讨会，贝叶斯深度学习](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrcr1zcpLZb7paw9LNgx-zsCr-PstjBM6)\n+ [神经信息处理系统研讨会，野外环境下的可靠机器学习](https:\u002F\u002Fsites.google.com\u002Fsite\u002Fwildml2016nips\u002Fschedule)\n+ [神经信息处理系统研讨会，近似推断](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL8Yb49e5zFuztzY4wZRp_XIj6PREg3pw8)\n+ [神经信息处理系统研讨会，对抗训练](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJscN9YDD1buxCitmej1pjJkR5PMhenTF)\n+ [神经信息处理系统](https:\u002F\u002Fnips.cc\u002FConferences\u002F2016\u002FSchedule)\n+ [湾区深度学习学校](https:\u002F\u002Fwww.bayareadlschool.org\u002F)\n+ [深度学习、工具与方法研讨会](https:\u002F\u002Fportal.klewel.com\u002Fwatch\u002Fwebcast\u002Fdeep-learning-tools-and-methods-workshop\u002F)\n+ [知识发现与数据挖掘](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCPsUUDUlcTJuP-fRa7z85aQ\u002Fplaylists)\n+ [蒙特利尔深度学习暑期学校](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2016_montreal\u002F)\n+ [计算中的不确定性，西蒙斯研究所](https:\u002F\u002Fsimons.berkeley.edu\u002Fworkshops\u002Flogic2016-1)\n+ [秘鲁机器学习暑期学校](http:\u002F\u002Fwww.ucsp.edu.pe\u002Fciet\u002Fmlss16\u002Fspeakers.html)\n+ [国际机器学习大会](http:\u002F\u002Ftechtalks.tv\u002Ficml\u002F2016\u002F)\n+ [加的斯机器学习暑期学校](http:\u002F\u002Flearning.mpi-sws.org\u002Fmlss2016\u002Fvideos\u002F)\n+ [国际表征学习大会](http:\u002F\u002Fvideolectures.net\u002Ficlr2016_san_juan\u002F)\n\n## 2015年\n\n+ [自然语言处理的实证方法](https:\u002F\u002Fvimeo.com\u002Fchannels\u002Femnlp2015)\n+ [计算机视觉与模式识别](http:\u002F\u002Ftechtalks.tv\u002Fcvpr\u002F2015\u002F)\n+ [国际计算机视觉大会](http:\u002F\u002Fvideolectures.net\u002Ficcv2015_santiago\u002F)\n+ [神经信息处理系统](https:\u002F\u002Fnips.cc\u002FConferences\u002F2015\u002FSchedule)\n+ [谢菲尔德高斯过程暑期学校](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhoHEZlJjdQJLRUSE9_55eXkwNXKTiNQF)\n+ [蒙特利尔深度学习暑期学校](http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2015_montreal\u002F)\n+ [国际机器学习大会](http:\u002F\u002Fvideolectures.net\u002Ficml2015_lille\u002F)\n+ [人工智能中的不确定性](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCXDf7Y4KMcqPWHriorcMDNg\u002Fvideos)\n+ [阿瑟·M·萨克勒座谈会：从大数据中推断因果关系](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLGJm1x3XQeK0NgFOX2Z7Wt-P5RU5Zv0Hv)\n+ [国际表征学习大会](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhiWXaTdsWB8PnrVZquVyqlRFWXM4ijYz)\n+ [学习理论会议](http:\u002F\u002Fvideolectures.net\u002Fcolt2015_paris\u002F)\n+ [图宾根机器学习暑期学校](http:\u002F\u002Fmlss.tuebingen.mpg.de\u002F2015\u002Fspeakers.html)\n+ [悉尼机器学习暑期学校](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCT1k2e63pqm_VSXmaF21n6g\u002Fvideos)\n\n## 2014年\n\n+ [计算机视觉与模式识别 - 口头报告](http:\u002F\u002Ftechtalks.tv\u002Fcvpr-2014-oral-talks\u002F)\n+ [计算机视觉与模式识别 - 精选亮点](http:\u002F\u002Ftechtalks.tv\u002Fcvpr-2014-video-highlights\u002F)\n+ [欧洲计算机视觉会议](http:\u002F\u002Fvideolectures.net\u002Feccv2014_zurich\u002F)\n+ [多伦多大学大数据与统计机器学习研讨会](http:\u002F\u002Fwww.fields.utoronto.ca\u002Fvideo-archive\u002Fevent\u002F316\u002F2014)\n+ [神经信息处理系统大会](https:\u002F\u002Fnips.cc\u002FConferences\u002F2014\u002FSchedule)\n+ [高斯过程夏季学校，谢菲尔德](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhoHEZlJjdQKgrpK70Ym04Ju3-9W-QXns)\n+ [机器学习夏季学校，匹兹堡](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQCIYxE3ycGLXHMjK3XV7Iz)\n+ [国际机器学习大会](http:\u002F\u002Ftechtalks.tv\u002Ficml2014\u002F)\n+ [学习理论会议](http:\u002F\u002Fvideolectures.net\u002Fcolt2014_barcelona\u002F)\n+ [人工智能与统计学](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCQeS3L6d-S6ZeCQChFyK5Uw)\n\n## 2013年\n\n+ [国际计算机视觉大会](http:\u002F\u002Ftechtalks.tv\u002Ficcv2013\u002F)\n+ [高斯过程冬季学校，谢菲尔德](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLhoHEZlJjdQKI1cs5yPRUYdgcsE0HctoQ)\n+ [机器学习夏季学校，冰岛](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC3ywjSv5OsDiDAnOP8C1NiQ)\n+ [神经信息处理系统大会](https:\u002F\u002Fnips.cc\u002FConferences\u002F2013\u002FSchedule)\n+ [神经信息处理系统研讨会](http:\u002F\u002Fvideolectures.net\u002Fnipsworkshops2013_laketahoe\u002F)\n+ [国际机器学习大会](http:\u002F\u002Ftechtalks.tv\u002Ficml2013\u002F)\n+ [机器学习夏季学校，图宾根](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E)\n+ [学习理论会议](http:\u002F\u002Fvideolectures.net\u002Fcolt2013_princeton\u002F)\n+ [哈佛大学定量社会科学研究所，应用统计学研讨会](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLoKvRqQVbtI-ZhBpBrKT2KllP5dH8O0M)\n\n## 2012年\n\n+ [布朗大学ICERM研讨会](https:\u002F\u002Ficerm.brown.edu\u002Fvideo_archive\u002F)（其中[2012年贝叶斯非参数方法](https:\u002F\u002Ficerm.brown.edu\u002Fsp-f12-w1\u002F)特别精彩）\n+ [神经信息处理系统大会](http:\u002F\u002Fvideolectures.net\u002Fnips2012_laketahoe\u002F)\n+ [人工智能中的不确定性](http:\u002F\u002Fvideolectures.net\u002Fuai2012_catalinaislands\u002F)\n+ [国际机器学习大会](http:\u002F\u002Ftechtalks.tv\u002Fsearch\u002Fresults\u002F?q=icml+2012)\n+ [AAAI人工智能大会](http:\u002F\u002Fvideolectures.net\u002Faaai2012_toronto\u002F)\n\n## 2011年\n\n+ [神经信息处理系统大会](http:\u002F\u002Fvideolectures.net\u002Fnips2011_granada\u002F)\n+ [神经信息处理系统研讨会](http:\u002F\u002Fvideolectures.net\u002Fnipsworkshops2011_sierranevada\u002F)\n+ [人工智能中的不确定性](http:\u002F\u002Fvideolectures.net\u002Fuai2011_barcelona\u002F)\n+ [学习理论会议](http:\u002F\u002Fvideolectures.net\u002Fcolt2011_budapest\u002F)\n+ [人工智能与统计学](http:\u002F\u002Fvideolectures.net\u002Faistats2011_fortlauderdale\u002F)\n\n## 2010年\n\n+ [神经信息处理系统大会](http:\u002F\u002Fvideolectures.net\u002Fnips2010_vancouver\u002F)\n+ [国际机器学习大会](http:\u002F\u002Fvideolectures.net\u002Ficml2010_haifa\u002F)\n+ [人工智能与统计学](http:\u002F\u002Fvideolectures.net\u002Faistats2010_sardinia\u002F)\n\n## 2009年\n\n+ [神经信息处理系统大会](http:\u002F\u002Fvideolectures.net\u002Fnips09_vancouver\u002F)\n+ [剑桥机器学习夏季学校](http:\u002F\u002Fvideolectures.net\u002Fmlss09uk_cambridge\u002F)\n+ [国际机器学习大会](http:\u002F\u002Fvideolectures.net\u002Ficml09_montreal\u002F)\n\n## 2008年\n\n+ [知识发现与数据挖掘](http:\u002F\u002Fvideolectures.net\u002Fkdd08_las_vegas\u002F)\n+ [人工智能中的不确定性](http:\u002F\u002Fvideolectures.net\u002Fuai08_helsinki\u002F)\n+ [国际机器学习大会](http:\u002F\u002Fvideolectures.net\u002Ficml08_helsinki\u002F)\n\n## 2007年\n\n+ [国际机器学习大会](http:\u002F\u002Fvideolectures.net\u002Ficml07_corvallis\u002F)\n\n## 2006年\n\n+ [神经信息处理系统大会](http:\u002F\u002Fvideolectures.net\u002Fnips06_whistler\u002F)\n\n## 2005年\n\n+ [神经信息处理系统研讨会：核方法](http:\u002F\u002Fvideolectures.net\u002Fnips05_whistler\u002F)\n+ [国际机器学习大会](http:\u002F\u002Fvideolectures.net\u002Ficml05_bonn\u002F)","# ml-videos 快速上手指南\n\n## 简介\n`ml-videos` 并非一个需要安装的软件库或命令行工具，而是一个**机器学习教育视频资源索引集合**。它整理了来自全球顶级机器学习会议（如 NeurIPS, ICML, CVPR）、研讨会、暑期学校及讲座的公开视频链接。\n\n本指南旨在帮助开发者快速定位并访问这些高质量的学习资源。\n\n## 环境准备\n由于本项目仅为链接列表，**无需安装任何依赖或配置特定运行环境**。您只需要：\n- 一台连接互联网的计算机或移动设备。\n- 现代网页浏览器（推荐 Chrome, Firefox, Edge）。\n- （可选）稳定的网络环境，部分视频托管于 YouTube 或 Vimeo，国内访问可能需要网络加速工具。\n\n## 获取资源\n无需执行安装命令，直接通过以下方式访问资源：\n\n1. **访问官方仓库页面**\n   前往 GitHub 项目主页查看最新整理的列表：\n   ```bash\n   # 在浏览器中打开\n   https:\u002F\u002Fgithub.com\u002Fdustinvtran\u002Fml-videos\n   ```\n\n2. **直接跳转视频平台**\n   根据下方分类，直接点击对应平台的链接进入频道或播放列表：\n   - **综合归档**: [VideoLectures.NET](http:\u002F\u002Fvideolectures.net\u002F) | [TechTalks.tv](http:\u002F\u002Ftechtalks.tv\u002F) | [Slideslive](https:\u002F\u002Fslideslive.com\u002F)\n   - **YouTube 精选**: [Nando de Freitas 教程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) | [Alex Smola 教程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHTTV7w9u7grTXBHMH-mw3qn) | [mathematicalmonk 基础数学](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLD0F06AA0D2E8FFBA)\n\n## 基本使用\n按照会议年份或领域查找对应的视频链接：\n\n### 1. 查找最新会议视频 (以 2020 年为例)\n若需学习最新的 **ICML 2020** 论文演讲，请直接访问 Slideslive 专区：\n> 链接：https:\u002F\u002Fslideslive.com\u002Ficml-2020\n\n若需查看 **CVPR 2020** 的论文与教程：\n> 链接：https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC0n76gicaarsN_Y9YShWwhw\u002Fplaylists\n\n### 2. 查找经典深度学习课程\n若需系统学习深度学习基础，推荐访问 **Deep Learning Summer School** 系列：\n- **2018 多伦多暑期学校**: http:\u002F\u002Fvideolectures.net\u002FDLRLsummerschool2018_toronto\u002F\n- **2017 蒙特利尔暑期学校**: http:\u002F\u002Fvideolectures.net\u002Fdeeplearning2017_montreal\u002F\n\n### 3. 按领域筛选\n- **自然语言处理 (NLP)**: 查阅 ACL, EMNLP, NAACL 历年频道（多托管于 Vimeo）。\n- **计算机视觉 (CV)**: 查阅 CVPR, ICCV, ECCV 历年频道（多托管于 YouTube）。\n- **强化学习与动态控制**: 查阅 L4DC 或 Deep RL Bootcamp 专题链接。\n\n> **提示**：该项目由社区维护，如果您发现新的优质资源视频，欢迎通过 GitHub 提交 Pull Request 更新列表。","某高校人工智能实验室的研究生团队正急需为“多模态学习”课题寻找最新的前沿讲座视频，以补充文献阅读的不足并准备组会分享。\n\n### 没有 ml-videos 时\n- 研究人员需要在 YouTube、Vimeo、SlidesLive 及各个会议官网之间反复跳转搜索，耗时数小时却难以确认资源完整性。\n- 面对分散的视频源，很难系统性地获取如 ICML、NeurIPS 等顶级会议从 2018 至 2020 年的完整研讨会（Workshop）录像，导致关键技术细节缺失。\n- 缺乏权威的分类指引，初学者容易在海量低质量视频中迷失，难以找到像 Nando de Freitas 或 Alex Smola 等专家的系统性教学课程。\n- 团队内部资料共享困难，每个人收集的资源格式不一、来源杂乱，无法形成统一的知识库供后续复现参考。\n\n### 使用 ml-videos 后\n- 团队直接访问 ml-videos 汇总页，一键获取涵盖 VideoLectures.NET、TechTalks.tv 等主流平台的精选链接，将资源搜集时间从数天缩短至几分钟。\n- 通过按年份和会议类型（如 CVPR、ICLR）清晰梳理的列表，迅速定位到特定主题的口头报告与教程视频，确保了学习材料的深度与广度。\n- 借助工具中推荐的优质频道清单，成员们能快速跟随顶尖学者的系列讲座建立扎实的理论框架，避免了在无效内容上浪费精力。\n- 统一的资源索引让团队成员能高效协作，基于相同的高质量视频源进行讨论和代码复现，显著提升了组会分享的专业度。\n\nml-videos 通过聚合分散的全球机器学习视频资源，将原本碎片化的信息搜集工作转化为高效系统的知识获取流程。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fdustinvtran_ml-videos_7bf27e95.png","dustinvtran","Dustin Tran","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fdustinvtran_e80e2027.jpg",null,"Google DeepMind","San Francisco, CA","dustintran.com","https:\u002F\u002Fgithub.com\u002Fdustinvtran",1555,212,"2026-04-02T08:34:54",1,"","未说明",{"notes":88,"python":86,"dependencies":89},"该工具并非可执行的软件或代码库，而是一个机器学习会议、研讨会和讲座的视频资源链接合集。用户只需通过浏览器访问 README 中列出的 YouTube、Vimeo、Slideslive 等外部链接即可观看内容，因此无需安装任何操作系统、GPU、内存、Python 环境或依赖库。",[],[14,91],"视频",[93,94,95,96,97,98],"machine-learning","video","workshops","summer-schools","conference","statistics","2026-03-27T02:49:30.150509","2026-04-20T04:06:10.980340",[],[]]