[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-mrsaeeddev--free-ai-resources":3,"tool-mrsaeeddev--free-ai-resources":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",154349,2,"2026-04-13T23:32:16",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":72,"owner_website":79,"owner_url":80,"languages":81,"stars":82,"forks":83,"last_commit_at":84,"license":85,"difficulty_score":86,"env_os":87,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":92,"github_topics":94,"view_count":32,"oss_zip_url":81,"oss_zip_packed_at":81,"status":17,"created_at":113,"updated_at":114,"faqs":115,"releases":116},7343,"mrsaeeddev\u002Ffree-ai-resources","free-ai-resources","🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!","free-ai-resources 是一个专为人工智能爱好者打造的免费资源聚合库，旨在打破学习门槛，让每个人都能轻松获取高质量的 AI 学习资料。面对人工智能领域知识更新快、优质课程分散且部分收费高昂的痛点，该项目精心整理并持续更新了一系列完全免费的资源，涵盖系统化的在线课程、前沿的研究论文、实用的技术博客以及相关的就业机会。\n\n无论是想要转行进入 AI 领域的开发者、需要追踪最新学术动态的研究人员，还是仅希望了解人工智能基础概念的普通用户，都能在这里找到适合自己的入门路径或进阶指南。其核心亮点在于“精选”与“免费”：不仅收录了来自斯坦福、哈佛、谷歌、微软等顶尖高校和科技巨头的权威课程（如 CS50、Elements of AI），还按类别清晰梳理，帮助用户节省筛选时间，快速构建知识体系。在 AI 技术日益渗透各行各业的今天，free-ai-resources 就像一位贴心的向导，助你零成本开启智能时代的探索之旅，掌握面向未来的关键技能。","\u003Cdiv align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmrsaeeddev\u002Ffree-ai-resources\">\n        \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmrsaeeddev_free-ai-resources_readme_8c7e60b2023a.png\">\n    \u003C\u002Fa>\n    \u003Cbr\u002F>\n    \u003Cbr\u002F>\n\n\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmrsaeeddev_free-ai-resources_readme_dbadffe11aa4.jpg\">\n\u003Cdiv align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"Last Commit Badge\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fw\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"Commit Activity Badge\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"Contributors Badge\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"License Badge\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fmrsaeeddev\u002Ffree-ai-resources?style=social\" alt=\"Stars Badge\"\u002F>\n\n> A curated list of FREE AI RESOURCES for aspiring AI Engineers\n\n\u003Chr \u002F>\n\u003Ca href=\"http:\u002F\u002Fsaeed.js.org\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=&labelColor=505050&message=website&color=%230076D6&style=flat&logo=google-chrome&logoColor=%230076D6\" alt=\"website\"\u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fmrsaeeddev\">\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fmrsaeeddev?label=Follow&style=social\" alt=\"Twitter Badge\"\u002F>\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Flinkedin.com\u002Fin\u002Fmrsaeeddev\">\n\u003Cimg src=\"https:\u002F\u002Fcamo.githubusercontent.com\u002F406fa0f807a6e4126cf965cf201f6197861d49e3\u002F68747470733a2f2f696d672e736869656c64732e696f2f747769747465722f75726c3f6c6162656c3d4c696e6b6564496e266c6f676f3d6c696e6b6564496e267374796c653d736f6369616c2675726c3d68747470732533412532462532467777772e6c696e6b6564696e2e636f6d253246696e253246716173696d2d68617373616e253246\"\u002F>\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmrsaeeddev\">\n\u003Cimg alt=\"GitHub followers\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Ffollowers\u002Fmrsaeeddev?label=Follow&style=social\"\u002F>\u003C\u002Fa>\n\u003C\u002Fdiv>\n\u003C\u002Fdiv>\n\n\u003Cbr\u002F>\n\n### WHAT IS AI?\n\nArtificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.\n\n### WHY CHOOSE AI?\n\nArtificial Intelligence is advancing by leaps and bounds. Recent research in the fields of Data Science, Machine Learning, Natural Language Processing and other sub fields of AI has already started to impact the lives of common people. AI is no more a superficial concept. It's already used by tech giants, companies and startups to solve everyday problems. That's why choosing AI as a career path is really rewarding in the long run.\n\nEven if your profession is not directly related to tech, still it's said that AI will disrupt every field in one or other ways. That's why you need to have at least a basic understanding of how AI works.\n\n### 🤖 FREE AI COURSES:\n- EdX’s Artificial Intelligence - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fartificial-intelligence-ai\n- Udacity’s Intro to Artificial Intelligence - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-artificial-intelligence--cs271\n- Artificial Intelligence: Principles and Techniques By Stanford - http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs221\u002F\n- Udacity’s Artificial Intelligence for Robotics by Georgia Tech - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fartificial-intelligence-for-robotics--cs373\n- IBM's Data Science and Cognitive Computing courses - https:\u002F\u002Fcognitiveclass.ai\u002F\n- Elements of AI - https:\u002F\u002Fwww.elementsofai.com\u002F\n- Building AI - https:\u002F\u002Fbuildingai.elementsofai.com\u002F\n- Intellipaat's Artificial Intelligence - https:\u002F\u002Fintellipaat.com\u002Facademy\u002Fcourse\u002Fartificial-intelligence-free-course\u002F\n- EdX\u002FHarvard University's CS50: Introduction to Artificial Intelligence with Python - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fcs50s-introduction-to-artificial-intelligence-with-python\n- Microsoft AI School - https:\u002F\u002Faischool.microsoft.com\u002Fen-us\u002Fhome\n- Learn with Google AI - https:\u002F\u002Fai.google\u002Feducation\u002F\n- Crash Course - Artificial Intelligence https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GvYYFloV0aA&list=PL8dPuuaLjXtO65LeD2p4_Sb5XQ51par_b\n\n### 🔢 FREE MATHEMATICS RESOURCES:\n#### Videos\n- All Levels\u002FPre-U - http:\u002F\u002Fwww.patrickjmt.com\u002F\n- All Levels\u002FPre-U - http:\u002F\u002Fwww.khanacademy.org\u002F\n- College - http:\u002F\u002Focw.mit.edu\u002FOcwWeb\u002Fweb\u002Fcourses\u002Fcourses\u002Findex.htm#Mathematics\n- College - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCoHhuummRZaIVX7bD4t2czg\n- College - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC2F-j2KMho0zVWIPFKWoXoA\u002Fvideos\n- College - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC5Y9H2KDRHZZTWZJtlH4VbA\n- All - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCNVMxRMEwvo9AS-Jfh6fQFg\n- College - http:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fnjwildberger\n- College - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FMathDoctorBob\n- High-School\u002F College - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCfbSz1B68ytEKX0D6AFdddQ\n- All Levels\u002F Pre-U - http:\u002F\u002Fwww.mathtv.com\u002F\n- All Levels\u002FPre-U - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fprofrobbob\n- All Levels\u002FPre-U - http:\u002F\u002Fwww.hippocampus.org\u002F\n- GCSE Level - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fschoolmaths\n\n#### For Fun\n- 3Blue1Brown https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCYO_jab_esuFRV4b17AJtAw\n- Mathologer https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC1_uAIS3r8Vu6JjXWvastJg\n- MathologerII - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCH74Hc_7WYVzx1GXhLEH6Eg\n- ViHart - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCOGeU-1Fig3rrDjhm9Zs_wg\n- MindYourDecisions - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCHnj59g7jezwTy5GeL8EA_g\n- Tipping-Point-Math - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCjwOWaOX-c-NeLnj_YGiNEg\n- WelchLabs - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUConVfxXodg78Tzh5nNu85Ew\n- Infinite Series - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCs4aHmggTfFrpkPcWSaBN9g\n- Vsauce - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC6nSFpj9HTCZ5t-N3Rm3-HA\n- Numberphile  https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCoxcjq-8xIDTYp3uz647V5A\n- Blackpenredpen https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fblackpenredpen\n- AI and Games youtube channel https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCov_51F0betb6hJ6Gumxg3Q\n- A.I. and Machine Learning in Unity, Sebastian Schuchmann youtube channel https:\u002F\u002Fwww.youtube.com\u002Fc\u002FSebastianSchuchmannAI\n\n#### Example problems and online notes\u002Frefrences\n- Example Problems - http:\u002F\u002Fwww.exampleproblems.com\u002F\n- Interact math - http:\u002F\u002Fwww.interactmath.com\u002F\n- Pauls online Math notes - http:\u002F\u002Ftutorial.math.lamar.edu\u002F\n- Calculus org -http:\u002F\u002Fwww.calculus.org\u002F\n- Wolfram Mathworld - http:\u002F\u002Fmathworld.wolfram.com\u002F\n- CTY Online AP & College Math Resources - https:\u002F\u002Fsites.google.com\u002Fa\u002Fctyonline.net\u002Fjdinoto\u002F\n- J.S. Milne's Site - http:\u002F\u002Fwww.jmilne.org\u002Fmath\u002F\n- History of Math - http:\u002F\u002Fwww-history.mcs.st-and.ac.uk\u002F\n- Harvey Mudd College's Online Math Tutorials - http:\u002F\u002Fwww.math.hmc.edu\u002Fcalculus\u002Ftutorials\u002F\n- Real (and some complex) Analysis & Programming - http:\u002F\u002Fwww.mathcs.org\u002F\n\n#### Computer Algebra Systems\n- SAGE - http:\u002F\u002Fwww.sagemath.org\u002Findex.html\n- Maxima - http:\u002F\u002Fmaxima.sourceforge.net\u002F\n- Octave - http:\u002F\u002Fwww.gnu.org\u002Fsoftware\u002Foctave\n- Wolfram Alpha- http:\u002F\u002Fwww.wolframalpha.com\u002F\n- Geogebra - http:\u002F\u002Fwww.geogebra.org\u002Fcms\n- PARI\u002FGP https:\u002F\u002Fpari.math.u-bordeaux.fr\u002F\n\n#### Graphics And Visualizing mathematics\n- GeoGebra - http:\u002F\u002Fwww.geogebra.org\u002Fcms\n- gnuplot - http:\u002F\u002Fwww.gnuplot.info\u002F\n- garminder - http:\u002F\u002Fwww.gapminder.org\u002F\n- Wolfram Demonstrations Project - http:\u002F\u002Fdemonstrations.wolfram.com\u002F\n- wolframa - http:\u002F\u002Fwww.wolframalpha.com\u002F\n- scipy- http:\u002F\u002Fwww.scipy.org\u002F\n- Microsoft Mathematics*  - http:\u002F\u002Fwww.microsoft.com\u002Fdownloads\u002Fen\u002Fdetails.aspx?FamilyID=9caca722-5235-401c-8d3f-9e242b794c3a\n- Winplot - http:\u002F\u002Fmath.exeter.edu\u002Frparris\u002Fwinplot.html\n- Desmos - http:\u002F\u002Fdesmos.com\u002Fcalculator\u002F\n- Symbolab - http:\u002F\u002Fwww.symbolab.com\u002F\n- Scilab - http:\u002F\u002Fwww.scilab.org\u002F\n\n#### TypeSetting  (Latex)\n- TeX Users Group - http:\u002F\u002Fwww.tug.org\u002F\n- The Comprehensive TeX Archive Network - http:\u002F\u002Fwww.ctan.org\u002F\n- Art of Problem Solving Tutorial - http:\u002F\u002Fwww.artofproblemsolving.com\u002FLaTeX\u002FAoPS_L_About.php\n- TexPaste - http:\u002F\u002Fwww.texpaste.com\u002F\n- Xfig - http:\u002F\u002Fwww.xfig.org\u002F\n- Detextify - http:\u002F\u002Fdetexify.kirelabs.org\u002Fclassify.html?\n- WriteLaTeX WYSIWYG - https:\u002F\u002Fwww.writelatex.com\u002F\n- LaTeX Examples - http:\u002F\u002Fwww.texample.net\u002F\n\n#### Blogs\u002FArticles\n- Terry Tao - http:\u002F\u002Fterrytao.wordpress.com\u002F\n- American Mathematical Society - http:\u002F\u002Fblogs.ams.org\u002Fblogonmathblogs\u002F\n- AMS notices - http:\u002F\u002Fwww.ams.org\u002Fnotices\u002F\n- The n-Category Café - https:\u002F\u002Fgolem.ph.utexas.edu\u002Fcategory\u002F\n- Tim Gowers - http:\u002F\u002Fgowers.wordpress.com\u002F\n- ADD\u002FXOR\u002FROL - http:\u002F\u002Faddxorrol.blogspot.com\u002F\n- Math with Bad Drawings - https:\u002F\u002Fmathwithbaddrawings.com\u002F\n- Math ∩ Programming - https:\u002F\u002Fjeremykun.com\u002F\n- Almost Looks Like Work - https:\u002F\u002Fjasmcole.com\u002F\n- Math3ma - https:\u002F\u002Fwww.math3ma.com\u002F\n- Qiaochu Yuan - https:\u002F\u002Fqchu.wordpress.com\u002F\n- Carlos Matheus - https:\u002F\u002Fmatheuscmss.wordpress.com\u002F\n- Burt Totaro - https:\u002F\u002Fburttotaro.wordpress.com\u002F\n- Igor Pak - https:\u002F\u002Figorpak.wordpress.com\u002F\n- Alex Youcis - https:\u002F\u002Fayoucis.wordpress.com\u002F\n- Low dimensional topology - https:\u002F\u002Fldtopology.wordpress.com\u002F\n- Jordan Ellenberg - https:\u002F\u002Fquomodocumque.wordpress.com\u002F\n- Secret Blogging Seminar - https:\u002F\u002Fsbseminar.wordpress.com\u002F\n- Math Wizurd - http:\u002F\u002Fwww.mathwizurd.com\u002Fcalc\n\n#### Misc\n- academicearth.org - http:\u002F\u002Fwww.academicearth.org\u002Fsubjects\u002Fmathematics\n- Encyclopedia of Mathematics - http:\u002F\u002Fwww.encyclopediaofmath.org\u002F\n- Large List of Recommended books, online resources - http:\u002F\u002Fhbpms.blogspot.com\u002F\n- Online Encyclopedia of Integer Sequences - http:\u002F\u002Fwww.research.att.com\u002F~njas\u002Fsequences\u002F\n- MathIM - http:\u002F\u002Fwww.mathim.com\u002F\n- Free Book on Neural Network and Deep Learning - http:\u002F\u002Fneuralnetworksanddeeplearning.com\u002F\n- Informational website on artificial intelligence - https:\u002F\u002Fintelligencereborn.com\n\n#### Other Lists of resources\n- Math Overflow's List of Free Online Lectures - http:\u002F\u002Fmathoverflow.net\u002Fquestions\u002F54430\u002Fvideo-lectures-of-mathematics-courses-available-online-for-free\n- Top-down Learning Path on Machine Learning for Software Engineers - https:\u002F\u002Fgithub.com\u002FZuzooVn\u002Fmachine-learning-for-software-engineers\n- Fun Learning Projects on Machine Learning for Beginners - https:\u002F\u002Felitedatascience.com\u002Fmachine-learning-projects-for-beginners\n\n\n\n\n### ⚙️ FREE MACHINE LEARNING COURSES:\n- Machine Learning by Andrew NG - https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning\n- Intro to ML by Udacity - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-machine-learning--ud120\n- EdX’s Learning from Data(Introductory Machine Learning) - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Flearning-from-data-introductory-machine-learning#!\n- Introduction to Machine Learning for Coders - http:\u002F\u002Fcourse18.fast.ai\u002Fml\n- Statistical Machine Learning by CMU - https:\u002F\u002Fwww.youtube.com\u002Fwatch?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE&v=zcMnu-3wkWo\n- Coursera’s Neural Networks for Machine Learning - https:\u002F\u002Fwww.youtube.com\u002Fwatch?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9&v=cbeTc-Urqak\n- Kaggle Complete Roadmap for Machine Learning - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Foverview\n- EdX’s Principles of Machine Learning - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fprinciples-of-machine-learning\n- Coursera’s Machine Learning Specialization - https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning\n- Machine Learning Crash Course by Google - https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course\n- Machine Learning Course at W3Schools - https:\u002F\u002Fwww.w3schools.com\u002Fpython\u002Fpython_ml_getting_started.asp\n- Intro to Machine Learning Course at Kaggle - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintro-to-machine-learning\n- Intermediate Machine Learning Course at Kaggle - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintermediate-machine-learning\n- Machine Learning with Python - https:\u002F\u002Fcognitiveclass.ai\u002Fcourses\u002Fmachine-learning-with-python\n\n### 📈 FREE DATA SCIENCE COURSES:\n- IBM Data Science Professional Certificate - https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fibm-data-science\n- Udacity Intro to Data Science - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-data-science--ud359\n- Introduction to Data Science in Python - https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fpython-data-analysis\n- Introduction to Data Science Revised - https:\u002F\u002Falison.com\u002Fcourse\u002Fintroduction-to-data-science-revised\n- A Crash Course in Data Science - https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-science-course\n\n### 🧠 FREE DEEP LEARNING COURSES:\n- Google’s Deep Learning Course- https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-tensorflow-for-deep-learning--ud187\n- Practical Deep Learning for Coders - https:\u002F\u002Fcourse.fast.ai\u002F\n- Deep Learning from the Foundations - https:\u002F\u002Fcourse.fast.ai\u002Fpart2\n- Intro to Deep Learning using Tensorflow and Keras Course at Kaggle - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintro-to-deep-learning\n- Free Book on Neural Network and Deep Learning - http:\u002F\u002Fneuralnetworksanddeeplearning.com\u002F\n\n### 🗣️ FREE NLP COURSES:\n- A Code-First Introduction to Natural Language Processing - https:\u002F\u002Fwww.fast.ai\u002F2019\u002F07\u002F08\u002Ffastai-nlp\u002F\n- Natural Language Processing Specialization by Deeplearning.ai - https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fnatural-language-processing\n- Natural Language Processing Course at Kaggle - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fnatural-language-processing\n\n### 👁️ FREE MACHINE LEARNING IN GRAPHICS AND VISION COURSES:\n- CVPR 2020: Neural Rendering - https:\u002F\u002Fwww.neuralrender.com\u002F\n\n### 🏆 DATA SCIENCE COMPETITION HOSTING PLATFORMS:\n- Kaggle - https:\u002F\u002Fwww.kaggle.com\u002F\n- Analytics Vidhya - https:\u002F\u002Fwww.analyticsvidhya.com\u002F\n- CrowdANALYTIX - https:\u002F\u002Fwww.crowdanalytix.com\u002Fcommunity\n- Innocentive - https:\u002F\u002Fwww.innocentive.com\u002Four-solvers\u002F\n- CodaLab - https:\u002F\u002Fcompetitions.codalab.org\u002F\n- ZINDI - https:\u002F\u002Fzindi.africa\u002Fabout\n- AIcrowd - https:\u002F\u002Fwww.aicrowd.com\u002F\n- Driven Data - https:\u002F\u002Fwww.drivendata.org\u002F\n- Numerai - https:\u002F\u002Fnumer.ai\u002F\n- Tianchi - https:\u002F\u002Ftianchi.aliyun.com\u002Fcompetition\u002FgameList\u002FactiveList\n- Omdena - https:\u002F\u002Fomdena.com\u002F\n- HackerEarth -https:\u002F\u002Fwww.hackerearth.com\u002Fhackathon\u002Fexplore\u002Ffield\u002Fmachine-learning\u002F\n\n### 📦 DATASET REPOSITORIES:\n- Data World - https:\u002F\u002Fdata.world\u002Fdatasets\u002Fopen-data\n- Dataset Search by Google - https:\u002F\u002Fdatasetsearch.research.google.com\u002F\n- Kaggle Dataset - https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\n- UCI Machine Learning Repository - https:\u002F\u002Farchive.ics.uci.edu\u002F\n- Microsoft Open datasets - https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fservices\u002Fopen-datasets\u002Fcatalog\u002F\n- UCR - http:\u002F\u002Ftimeseriesclassification.com\u002F\n- Tensorflow Datasets - https:\u002F\u002Fwww.tensorflow.org\u002Fdatasets\u002Fcatalog\u002Foverview\n- Quandl - https:\u002F\u002Fwww.quandl.com\u002F\n\n### 🔬 AI RESEARCH AT BIG COMPANIES:\n- Machine Learning at Apple - https:\u002F\u002Fmachinelearning.apple.com\u002F\n- AI at Uber - https:\u002F\u002Fwww.uber.com\u002Fus\u002Fen\u002Fuberai\u002F\n- Machine Learning at Careem - https:\u002F\u002Fblog.careem.com\u002Fen\u002Ftag\u002Fmachine-learning\u002F\n- Data Science at Grab - https:\u002F\u002Fengineering.grab.com\u002Fcategories\u002Fdata-science\u002F\n- Autopilot AI at Tesla - https:\u002F\u002Fwww.tesla.com\u002FautopilotAI\n- AI at Microsoft - https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fai\n- AI Research at Google - https:\u002F\u002Fai.google\u002Fresearch\u002F\n- Self Driving Car Research at Lyft - https:\u002F\u002Fmedium.com\u002Flyftlevel5\n- AI Research at Huawei - https:\u002F\u002Fwww.huawei.com\u002Fen\u002Findustry-insights\u002Ftechnology\u002Fai\n- AI Research at Samsung - https:\u002F\u002Fresearch.samsung.com\u002Fartificial-intelligence\n- AI at Alibaba - https:\u002F\u002Fdamo.alibaba.com\u002Flabs\u002Fai\n- Data Science at Gojek - https:\u002F\u002Fblog.gojekengineering.com\u002Fdata-science\u002Fhome \n- Intelligent Transportation Technology and Security at Didi Chuxing - http:\u002F\u002Fwww.didi-labs.com\u002F\n- Amazon Science - https:\u002F\u002Fwww.amazon.science\u002F\n- Data Science at Bolt - https:\u002F\u002Fmedium.com\u002F@boltapp\n- Industrial AI Research at Hitachi - https:\u002F\u002Fwww.hitachi.com\u002Frd\u002Fsc\u002Faiblog\u002Findex.html\n\n### 💻 DEVELOPER RESOURCES:\n- Apple - https:\u002F\u002Fdeveloper.apple.com\u002Fmachine-learning\u002F\n- Facebook - https:\u002F\u002Fai.facebook.com\u002Ftools\u002F\n- Google - https:\u002F\u002Fcloud.google.com\u002Fproducts\u002Fai\n- Microsoft - https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fai\u002F\n\n### 🎥 YOUTUBE CHANNELS:\n- The Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FMITCSAIL\u002Fvideos\n- The Allen Institute for Artificial Intelligence - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCEqgmyWChwvt6MFGGlmUQCQ\u002Fvideos\n- DeepMind - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCP7jMXSY2xbc3KCAE0MHQ-A\u002Fvideos\n- Applied AI Course - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCJINtWke3-FMz2WuEltWDVQ\u002Fvideos\n- StarCraft Artificial Intelligence Tournament - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fcerticky\u002Fvideos\n- Sentdex - Data Analysis Tutorials - https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fsentdex\u002Fvideos\n- Amazon - Machine Learning University - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC12LqyqTQYbXatYS9AA7Nuw\n- Microsoft Research - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FMicrosoftResearch\n- Krish Nayak for ML\u002FDL\u002FData Science - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fkrishnaik06\n- TechWithTim - Python and ML Tutorials - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC4JX40jDee_tINbkjycV4Sg\n- Jabrils https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCQALLeQPoZdZC4JNUboVEUg\n\n### 💼 AI JOB SITES:\n- DataYoshi - https:\u002F\u002Fwww.datayoshi.com\u002F\n- AI Jobs - https:\u002F\u002Faijobs.com\u002F\n- AI-Jobs - https:\u002F\u002Fai-jobs.net\u002F\n- Indeed - https:\u002F\u002Fwww.indeed.com\u002Fq-Artificial-Intelligence-jobs.html\n- Kaggle Jobs - https:\u002F\u002Fwww.kaggle.com\u002Fjobs\n- Remote AI\u002FML Jobs: https:\u002F\u002Fwww.remoteaijobs.com\u002F\n- AI Jobs Board: https:\u002F\u002Faijobsboard.com\u002F\n\n### 📝 AI BLOGS:\n- Towards Data Science: https:\u002F\u002Ftowardsdatascience.com\u002F\n- Towards Machine Learning: https:\u002F\u002Ftowardsml.com\u002F\n- Towards AI: https:\u002F\u002Fmedium.com\u002Ftowards-artificial-intelligence\n- Fritz AI: https:\u002F\u002Fheartbeat.fritz.ai\u002F\n- The Batch: https:\u002F\u002Fwww.deeplearning.ai\u002Fthebatch\u002F\n- AI Trends: https:\u002F\u002Fwww.aitrends.com\u002F\n- DeepMind: https:\u002F\u002Fdeepmind.com\u002Fblog\n- Becoming HumanAI: https:\u002F\u002Fbecominghuman.ai\n- Berkeley Artificial Intelligence Research: https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F\n- IBM Developer: https:\u002F\u002Fdeveloper.ibm.com\u002Fpatterns\u002Fcategory\u002Fartificial-intelligence\u002F\n- OpenAI: https:\u002F\u002Fopenai.com\u002F\n- MIT News: https:\u002F\u002Fnews.mit.edu\u002Ftopic\u002Fartificial-intelligence2\n- Baidu Research: http:\u002F\u002Fresearch.baidu.com\u002F\n- Algorithmia: https:\u002F\u002Falgorithmia.com\u002Fblog\n- Machine Learning Mastery: https:\u002F\u002Fmachinelearningmastery.com\u002Fblog\u002F\n- Learn OpenCV: https:\u002F\u002Fwww.learnopencv.com\u002F\n\n### AI CHEAT-SHEETS:\n- Best of AI Cheat-Sheets: https:\u002F\u002Fbecominghuman.ai\u002Fcheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-science-pdf-f22dc900d2d7\n- Stanford CS229 Machine Learning: https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\n- Stanford CS230 Deep Learning: https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-230-deep-learning\n- Stanford CS221 Artificial Intelligence: https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-221-artificial-intelligence\n- Collection of AI Cheat-Sheets: http:\u002F\u002Fwww.aicheatsheets.com\u002F\n\n# CONTRIBUTION GUIDELINES:\nFeel free to open a PR if you feel like something needs to be added or may be you want to suggest something.\nIf you want to add something then your commit message should be like: **added \u003Cresource_name> to \u003Csection_name>**\n\n##### 🌟 Please star the repo so that it gets maximum exposure and more people can benefit from it!\n\n*Important Notice: All product names, logos, and brands are property of their respective owners. All company, product and service names used in this repository are for identification purposes only. Use of these names, logos, and brands does not imply endorsement.*\n","\u003Cdiv align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmrsaeeddev\u002Ffree-ai-resources\">\n        \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmrsaeeddev_free-ai-resources_readme_8c7e60b2023a.png\">\n    \u003C\u002Fa>\n    \u003Cbr\u002F>\n    \u003Cbr\u002F>\n\n\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmrsaeeddev_free-ai-resources_readme_dbadffe11aa4.jpg\">\n\u003Cdiv align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flast-commit\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"最新提交徽章\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcommit-activity\u002Fw\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"提交活跃度徽章\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fcontributors\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"贡献者徽章\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fmrsaeeddev\u002Ffree-ai-resources\" alt=\"许可证徽章\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fmrsaeeddev\u002Ffree-ai-resources?style=social\" alt=\"星标徽章\"\u002F>\n\n> 为有志于成为人工智能工程师的人士精心整理的免费AI资源列表\n\n\u003Chr \u002F>\n\u003Ca href=\"http:\u002F\u002Fsaeed.js.org\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=&labelColor=505050&message=网站&color=%230076D6&style=flat&logo=google-chrome&logoColor=%230076D6\" alt=\"网站\"\u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Ftwitter.com\u002Fmrsaeeddev\">\n\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fmrsaeeddev?label=关注&style=social\" alt=\"Twitter徽章\"\u002F>\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Flinkedin.com\u002Fin\u002Fmrsaeeddev\">\n\u003Cimg src=\"https:\u002F\u002Fcamo.githubusercontent.com\u002F406fa0f807a6e4126cf965cf201f6197861d49e3\u002F68747470733a2f2f696d672e7368696564732e696f2f747769747465722f75726c3f6c6162656c3d4c696e6b6564496e266c6f676f3d6c696e6b6564496e267374796c653d736f6369616c2675726c3d68747470732533412532467777772e6c696e6b6564696e2e636f6d253246696e253246716173696d2d68617373616e253246\"\u002F>\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fmrsaeeddev\">\n\u003Cimg alt=\"GitHub粉丝数\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Ffollowers\u002Fmrsaeeddev?label=关注&style=social\"\u002F>\n\u003C\u002Fa>\n\u003C\u002Fdiv>\n\u003C\u002Fdiv>\n\n\u003Cbr\u002F>\n\n### 什么是人工智能？\n\n人工智能（AI）是指通过编程使机器模拟人类智能，从而像人类一样思考并模仿人类行为的技术。该术语也可用于描述任何表现出与人类思维相关特征的机器，例如学习和解决问题的能力。\n\n### 为什么选择人工智能？\n\n人工智能正以惊人的速度发展。近年来，在数据科学、机器学习、自然语言处理及其他人工智能子领域中的研究已经开始影响普通人的生活。如今，人工智能已不再是一个表面化的概念，而是被科技巨头、企业和初创公司广泛应用于解决日常问题。因此，从长远来看，选择人工智能作为职业发展方向是非常值得的。\n\n即使你的职业与技术没有直接关系，也普遍认为人工智能将在各个领域以某种方式带来变革。因此，至少掌握一些关于人工智能工作原理的基础知识是很有必要的。\n\n### 🤖 免费人工智能课程：\n- edX的人工智能课程 - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fartificial-intelligence-ai\n- Udacity的人工智能入门课程 - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-artificial-intelligence--cs271\n- 斯坦福大学的人工智能：原理与技术课程 - http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs221\u002F\n- Udacity与佐治亚理工学院合作的人工智能机器人课程 - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fartificial-intelligence-for-robotics--cs373\n- IBM的数据科学与认知计算课程 - https:\u002F\u002Fcognitiveclass.ai\u002F\n- AI要素课程 - https:\u002F\u002Fwww.elementsofai.com\u002F\n- 构建AI课程 - https:\u002F\u002Fbuildingai.elementsofai.com\u002F\n- Intellipaat的人工智能免费课程 - https:\u002F\u002Fintellipaat.com\u002Facademy\u002Fcourse\u002Fartificial-intelligence-free-course\u002F\n- edX\u002F哈佛大学的CS50：使用Python的人工智能导论课程 - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fcs50s-introduction-to-artificial-intelligence-with-python\n- 微软人工智能学院 - https:\u002F\u002Faischool.microsoft.com\u002Fen-us\u002Fhome\n- Google AI学习平台 - https:\u002F\u002Fai.google\u002Feducation\u002F\n- 速成课程——人工智能 https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GvYYFloV0aA&list=PL8dPuuaLjXtO65LeD2p4_Sb5XQ51par_b\n\n### 🔢 免费数学资源：\n#### 视频\n- 各级别\u002F预科 - http:\u002F\u002Fwww.patrickjmt.com\u002F\n- 各级别\u002F预科 - http:\u002F\u002Fwww.khanacademy.org\u002F\n- 大学 - http:\u002F\u002Focw.mit.edu\u002FOcwWeb\u002Fweb\u002Fcourses\u002Fcourses\u002Findex.htm#Mathematics\n- 大学 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCoHhuummRZaIVX7bD4t2czg\n- 大学 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC2F-j2KMho0zVWIPFKWoXoA\u002Fvideos\n- 大学 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC5Y9H2KDRHZZTWZJtlH4VbA\n- 所有级别 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCNVMxRMEwvo9AS-Jfh6fQFg\n- 大学 - http:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fnjwildberger\n- 大学 - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FMathDoctorBob\n- 高中\u002F大学 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCfbSz1B68ytEKX0D6AFdddQ\n- 各级别\u002F预科 - http:\u002F\u002Fwww.mathtv.com\u002F\n- 各级别\u002F预科 - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fprofrobbob\n- 各级别\u002F预科 - http:\u002F\u002Fwww.hippocampus.org\u002F\n- GCSE级别 - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fschoolmaths\n\n#### 供娱乐\n- 3Blue1Brown https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCYO_jab_esuFRV4b17AJtAw\n- Mathologer https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC1_uAIS3r8Vu6JjXWvastJg\n- MathologerII - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCH74Hc_7WYVzx1GXhLEH6Eg\n- ViHart - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCOGeU-1Fig3rrDjhm9Zs_wg\n- MindYourDecisions - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCHnj59g7jezwTy5GeL8EA_g\n- Tipping-Point-Math - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCjwOWaOX-c-NeLnj_YGiNEg\n- WelchLabs - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUConVfxXodg78Tzh5nNu85Ew\n- Infinite Series - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCs4aHmggTfFrpkPcWSaBN9g\n- Vsauce - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC6nSFpj9HTCZ5t-N3Rm3-HA\n- Numberphile  https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCoxcjq-8xIDTYp3uz647V5A\n- Blackpenredpen https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fblackpenredpen\n- AI和游戏YouTube频道 https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCov_51F0betb6hJ6Gumxg3Q\n- Unity中的人工智能与机器学习，Sebastian Schuchmann的YouTube频道 https:\u002F\u002Fwww.youtube.com\u002Fc\u002FSebastianSchuchmannAI\n\n#### 示例题及在线笔记\u002F参考\n- 示例题 - http:\u002F\u002Fwww.exampleproblems.com\u002F\n- Interact math - http:\u002F\u002Fwww.interactmath.com\u002F\n- Paul's在线数学笔记 - http:\u002F\u002Ftutorial.math.lamar.edu\u002F\n- Calculus org -http:\u002F\u002Fwww.calculus.org\u002F\n- Wolfram Mathworld - http:\u002F\u002Fmathworld.wolfram.com\u002F\n- CTY Online AP & 大学数学资源 - https:\u002F\u002Fsites.google.com\u002Fa\u002Fctyonline.net\u002Fjdinoto\u002F\n- J.S. Milne的网站 - http:\u002F\u002Fwww.jmilne.org\u002Fmath\u002F\n- 数学史 - http:\u002F\u002Fwww-history.mcs.st-and.ac.uk\u002F\n- 哈维穆德学院的在线数学教程 - http:\u002F\u002Fwww.math.hmc.edu\u002Fcalculus\u002Ftutorials\u002F\n- 实分析（及部分复分析）与编程 - http:\u002F\u002Fwww.mathcs.org\u002F\n\n#### 计算机代数系统\n- SAGE - http:\u002F\u002Fwww.sagemath.org\u002Findex.html\n- Maxima - http:\u002F\u002Fmaxima.sourceforge.net\u002F\n- Octave - http:\u002F\u002Fwww.gnu.org\u002Fsoftware\u002Foctave\n- Wolfram Alpha- http:\u002F\u002Fwww.wolframalpha.com\u002F\n- Geogebra - http:\u002F\u002Fwww.geogebra.org\u002Fcms\n- PARI\u002FGP https:\u002F\u002Fpari.math.u-bordeaux.fr\u002F\n\n#### 图形与数学可视化\n- GeoGebra - http:\u002F\u002Fwww.geogebra.org\u002Fcms\n- gnuplot - http:\u002F\u002Fwww.gnuplot.info\u002F\n- garminder - http:\u002F\u002Fwww.gapminder.org\u002F\n- Wolfram演示项目 - http:\u002F\u002Fdemonstrations.wolfram.com\u002F\n- wolframa - http:\u002F\u002Fwww.wolframalpha.com\u002F\n- scipy- http:\u002F\u002Fwww.scipy.org\u002F\n- Microsoft Mathematics*  - http:\u002F\u002Fwww.microsoft.com\u002Fdownloads\u002Fen\u002Fdetails.aspx?FamilyID=9caca722-5235-401c-8d3f-9e242b794c3a\n- Winplot - http:\u002F\u002Fmath.exeter.edu\u002Frparris\u002Fwinplot.html\n- Desmos - http:\u002F\u002Fdesmos.com\u002Fcalculator\u002F\n- Symbolab - http:\u002F\u002Fwww.symbolab.com\u002F\n- Scilab - http:\u002F\u002Fwww.scilab.org\u002F\n\n#### 排版（LaTeX）\n- TeX用户组 - http:\u002F\u002Fwww.tug.org\u002F\n- 综合TeX档案网络 - http:\u002F\u002Fwww.ctan.org\u002F\n- 算法艺术问题解决教程 - http:\u002F\u002Fwww.artofproblemsolving.com\u002FLaTeX\u002FAoPS_L_About.php\n- TexPaste - http:\u002F\u002Fwww.texpaste.com\u002F\n- Xfig - http:\u002F\u002Fwww.xfig.org\u002F\n- Detextify - http:\u002F\u002Fdetexify.kirelabs.org\u002Fclassify.html?\n- WriteLaTeX WYSIWYG - https:\u002F\u002Fwww.writelatex.com\u002F\n- LaTeX示例 - http:\u002F\u002Fwww.texample.net\u002F\n\n#### 博客\u002F文章\n- Terry Tao - http:\u002F\u002Fterrytao.wordpress.com\u002F\n- 美国数学会 - http:\u002F\u002Fblogs.ams.org\u002Fblogonmathblogs\u002F\n- AMS通知 - http:\u002F\u002Fwww.ams.org\u002Fnotices\u002F\n- n-Category Café - https:\u002F\u002Fgolem.ph.utexas.edu\u002Fcategory\u002F\n- Tim Gowers - http:\u002F\u002Fgowers.wordpress.com\u002F\n- ADD\u002FXOR\u002FROL - http:\u002F\u002Faddxorrol.blogspot.com\u002F\n- Math with Bad Drawings - https:\u002F\u002Fmathwithbaddrawings.com\u002F\n- Math ∩ Programming - https:\u002F\u002Fjeremykun.com\u002F\n- Almost Looks Like Work - https:\u002F\u002Fjasmcole.com\u002F\n- Math3ma - https:\u002F\u002Fwww.math3ma.com\u002F\n- Qiaochu Yuan - https:\u002F\u002Fqchu.wordpress.com\u002F\n- Carlos Matheus - https:\u002F\u002Fmatheuscmss.wordpress.com\u002F\n- Burt Totaro - https:\u002F\u002Fburttotaro.wordpress.com\u002F\n- Igor Pak - https:\u002F\u002Figorpak.wordpress.com\u002F\n- Alex Youcis - https:\u002F\u002Fayoucis.wordpress.com\u002F\n- 低维拓扑 - https:\u002F\u002Fldtopology.wordpress.com\u002F\n- Jordan Ellenberg - https:\u002F\u002Fquomodocumque.wordpress.com\u002F\n- Secret Blogging Seminar - https:\u002F\u002Fsbseminar.wordpress.com\u002F\n- Math Wizurd - http:\u002F\u002Fwww.mathwizurd.com\u002Fcalc\n\n#### 其他\n- academicearth.org - http:\u002F\u002Fwww.academicearth.org\u002Fsubjects\u002Fmathematics\n- 数学百科全书 - http:\u002F\u002Fwww.encyclopediaofmath.org\u002F\n- 大量推荐书籍及在线资源列表 - http:\u002F\u002Fhbpms.blogspot.com\u002F\n- 整数序列在线百科全书 - http:\u002F\u002Fwww.research.att.com\u002F~njas\u002Fsequences\u002F\n- MathIM - http:\u002F\u002Fwww.mathim.com\u002F\n- 关于神经网络和深度学习的免费书籍 - http:\u002F\u002Fneuralnetworksanddeeplearning.com\u002F\n- 有关人工智能的信息网站 - https:\u002F\u002Fintelligencereborn.com\n\n#### 其他资源列表\n- Math Overflow的免费在线讲座列表 - http:\u002F\u002Fmathoverflow.net\u002Fquestions\u002F54430\u002Fvideo-lectures-of-mathematics-courses-available-online-for-free\n- 面向软件工程师的自顶向下机器学习学习路径 - https:\u002F\u002Fgithub.com\u002FZuzooVn\u002Fmachine-learning-for-software-engineers\n- 面向初学者的有趣机器学习项目 - https:\u002F\u002Felitedatascience.com\u002Fmachine-learning-projects-for-beginners\n\n### ⚙️ 免费机器学习课程：\n- 吴恩达的机器学习课程 - https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning\n- Udacity的机器学习入门课程 - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-machine-learning--ud120\n- EdX的从数据中学习（机器学习入门）课程 - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Flearning-from-data-introductory-machine-learning#!\n- 面向编码者的机器学习入门课程 - http:\u002F\u002Fcourse18.fast.ai\u002Fml\n- CMU的统计机器学习课程 - https:\u002F\u002Fwww.youtube.com\u002Fwatch?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE&v=zcMnu-3wkWo\n- Coursera的机器学习神经网络课程 - https:\u002F\u002Fwww.youtube.com\u002Fwatch?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9&v=cbeTc-Urqak\n- Kaggle机器学习完整路线图 - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Foverview\n- EdX的机器学习原理课程 - https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fprinciples-of-machine-learning\n- Coursera的机器学习专项课程 - https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning\n- Google的机器学习速成课程 - https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course\n- W3Schools的机器学习课程 - https:\u002F\u002Fwww.w3schools.com\u002Fpython\u002Fpython_ml_getting_started.asp\n- Kaggle的机器学习入门课程 - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintro-to-machine-learning\n- Kaggle的中级机器学习课程 - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintermediate-machine-learning\n- 使用Python的机器学习课程 - https:\u002F\u002Fcognitiveclass.ai\u002Fcourses\u002Fmachine-learning-with-python\n\n### 📈 免费数据科学课程：\n- IBM数据科学专业证书课程 - https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fibm-data-science\n- Udacity的数据科学入门课程 - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-data-science--ud359\n- Python中的数据科学入门课程 - https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fpython-data-analysis\n- 数据科学入门（修订版）课程 - https:\u002F\u002Falison.com\u002Fcourse\u002Fintroduction-to-data-science-revised\n- 数据科学速成课程 - https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-science-course\n\n### 🧠 免费深度学习课程：\n- Google的深度学习课程 - https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-tensorflow-for-deep-learning--ud187\n- 面向编码者的实用深度学习课程 - https:\u002F\u002Fcourse.fast.ai\u002F\n- 从基础开始的深度学习课程 - https:\u002F\u002Fcourse.fast.ai\u002Fpart2\n- Kaggle的使用TensorFlow和Keras的深度学习入门课程 - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintro-to-deep-learning\n- 关于神经网络和深度学习的免费书籍 - http:\u002F\u002Fneuralnetworksanddeeplearning.com\u002F\n\n### 🗣️ 免费自然语言处理课程：\n- 以代码为导向的自然语言处理入门课程 - https:\u002F\u002Fwww.fast.ai\u002F2019\u002F07\u002F08\u002Ffastai-nlp\u002F\n- Deeplearning.ai的自然语言处理专项课程 - https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fnatural-language-processing\n- Kaggle的自然语言处理课程 - https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fnatural-language-processing\n\n### 👁️ 免费图形与视觉领域的机器学习课程：\n- CVPR 2020：神经渲染 - https:\u002F\u002Fwww.neuralrender.com\u002F\n\n### 🏆 数据科学竞赛举办平台：\n- Kaggle - https:\u002F\u002Fwww.kaggle.com\u002F\n- Analytics Vidhya - https:\u002F\u002Fwww.analyticsvidhya.com\u002F\n- CrowdANALYTIX - https:\u002F\u002Fwww.crowdanalytix.com\u002Fcommunity\n- Innocentive - https:\u002F\u002Fwww.innocentive.com\u002Four-solvers\u002F\n- CodaLab - https:\u002F\u002Fcompetitions.codalab.org\u002F\n- ZINDI - https:\u002F\u002Fzindi.africa\u002Fabout\n- AIcrowd - https:\u002F\u002Fwww.aicrowd.com\u002F\n- Driven Data - https:\u002F\u002Fwww.drivendata.org\u002F\n- Numerai - https:\u002F\u002Fnumer.ai\u002F\n- Tianchi - https:\u002F\u002Ftianchi.aliyun.com\u002Fcompetition\u002FgameList\u002FactiveList\n- Omdena - https:\u002F\u002Fomdena.com\u002F\n- HackerEarth -https:\u002F\u002Fwww.hackerearth.com\u002Fhackathon\u002Fexplore\u002Ffield\u002Fmachine-learning\u002F\n\n### 📦 数据集仓库：\n- Data World - https:\u002F\u002Fdata.world\u002Fdatasets\u002Fopen-data\n- Google的数据集搜索 - https:\u002F\u002Fdatasetsearch.research.google.com\u002F\n- Kaggle数据集 - https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\n- UCI机器学习存储库 - https:\u002F\u002Farchive.ics.uci.edu\u002F\n- 微软开放数据集 - https:\u002F\u002Fazure.microsoft.com\u002Fen-us\u002Fservices\u002Fopen-datasets\u002Fcatalog\u002F\n- UCR - http:\u002F\u002Ftimeseriesclassification.com\u002F\n- TensorFlow数据集 - https:\u002F\u002Fwww.tensorflow.org\u002Fdatasets\u002Fcatalog\u002Foverview\n- Quandl - https:\u002F\u002Fwww.quandl.com\u002F\n\n### 🔬 大公司的人工智能研究：\n- 苹果的机器学习 - https:\u002F\u002Fmachinelearning.apple.com\u002F\n- Uber的人工智能 - https:\u002F\u002Fwww.uber.com\u002Fus\u002Fen\u002Fuberai\u002F\n- Careem的机器学习 - https:\u002F\u002Fblog.careem.com\u002Fen\u002Ftag\u002Fmachine-learning\u002F\n- Grab的数据科学 - https:\u002F\u002Fengineering.grab.com\u002Fcategories\u002Fdata-science\u002F\n- 特斯拉的自动驾驶人工智能 - https:\u002F\u002Fwww.tesla.com\u002FautopilotAI\n- 微软的人工智能 - https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fai\n- 谷歌的人工智能研究 - https:\u002F\u002Fai.google\u002Fresearch\u002F\n- Lyft的自动驾驶汽车研究 - https:\u002F\u002Fmedium.com\u002Flyftlevel5\n- 华为的人工智能研究 - https:\u002F\u002Fwww.huawei.com\u002Fen\u002Findustry-insights\u002Ftechnology\u002Fai\n- 三星的人工智能研究 - https:\u002F\u002Fresearch.samsung.com\u002Fartificial-intelligence\n- 阿里巴巴的人工智能 - https:\u002F\u002Fdamo.alibaba.com\u002Flabs\u002Fai\n- Gojek的数据科学 - https:\u002F\u002Fblog.gojekengineering.com\u002Fdata-science\u002Fhome\n- 滴滴出行的智能交通技术和安全 - http:\u002F\u002Fwww.didi-labs.com\u002F\n- Amazon Science - https:\u002F\u002Fwww.amazon.science\u002F\n- Bolt的数据科学 - https:\u002F\u002Fmedium.com\u002F@boltapp\n- 日立的工业人工智能研究 - https:\u002F\u002Fwww.hitachi.com\u002Frd\u002Fsc\u002Faiblog\u002Findex.html\n\n### 💻 开发者资源：\n- 苹果 - https:\u002F\u002Fdeveloper.apple.com\u002Fmachine-learning\u002F\n- Facebook - https:\u002F\u002Fai.facebook.com\u002Ftools\u002F\n- 谷歌 - https:\u002F\u002Fcloud.google.com\u002Fproducts\u002Fai\n- 微软 - https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fai\u002F\n\n### 🎥 YouTube频道：\n- 麻省理工学院计算机科学与人工智能实验室 - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FMITCSAIL\u002Fvideos\n- 艾伦人工智能研究所 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCEqgmyWChwvt6MFGGlmUQCQ\u002Fvideos\n- DeepMind - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCP7jMXSY2xbc3KCAE0MHQ-A\u002Fvideos\n- 应用人工智能课程 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCJINtWke3-FMz2WuEltWDVQ\u002Fvideos\n- 星际争霸人工智能锦标赛 - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fcerticky\u002Fvideos\n- Sentdex - 数据分析教程 - https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fsentdex\u002Fvideos\n- Amazon - 机器学习大学 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC12LqyqTQYbXatYS9AA7Nuw\n- 微软研究院 - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FMicrosoftResearch\n- Krish Nayak的ML\u002FDL\u002F数据科学内容 - https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fkrishnaik06\n- TechWithTim - Python和机器学习教程 - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUC4JX40jDee_tINbkjycV4Sg\n- Jabrils - https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCQALLeQPoZdZC4JNUboVEUg\n\n### 💼 人工智能职位网站：\n- DataYoshi - https:\u002F\u002Fwww.datayoshi.com\u002F\n- AI Jobs - https:\u002F\u002Faijobs.com\u002F\n- AI-Jobs - https:\u002F\u002Fai-jobs.net\u002F\n- Indeed - https:\u002F\u002Fwww.indeed.com\u002Fq-Artificial-Intelligence-jobs.html\n- Kaggle招聘 - https:\u002F\u002Fwww.kaggle.com\u002Fjobs\n- 远程人工智能\u002F机器学习职位：https:\u002F\u002Fwww.remoteaijobs.com\u002F\n- AI职位板：https:\u002F\u002Faijobsboard.com\u002F\n\n### 📝 人工智能博客：\n- Towards Data Science：https:\u002F\u002Ftowardsdatascience.com\u002F\n- Towards Machine Learning：https:\u002F\u002Ftowardsml.com\u002F\n- Towards AI：https:\u002F\u002Fmedium.com\u002Ftowards-artificial-intelligence\n- Fritz AI：https:\u002F\u002Fheartbeat.fritz.ai\u002F\n- The Batch：https:\u002F\u002Fwww.deeplearning.ai\u002Fthebatch\u002F\n- AI Trends：https:\u002F\u002Fwww.aitrends.com\u002F\n- DeepMind：https:\u002F\u002Fdeepmind.com\u002Fblog\n- Becoming HumanAI：https:\u002F\u002Fbecominghuman.ai\n- 伯克利人工智能研究实验室：https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F\n- IBM Developer：https:\u002F\u002Fdeveloper.ibm.com\u002Fpatterns\u002Fcategory\u002Fartificial-intelligence\u002F\n- OpenAI：https:\u002F\u002Fopenai.com\u002F\n- MIT新闻：https:\u002F\u002Fnews.mit.edu\u002Ftopic\u002Fartificial-intelligence2\n- 百度研究院：http:\u002F\u002Fresearch.baidu.com\u002F\n- Algorithmia：https:\u002F\u002Falgorithmia.com\u002Fblog\n- Machine Learning Mastery：https:\u002F\u002Fmachinelearningmastery.com\u002Fblog\u002F\n- Learn OpenCV：https:\u002F\u002Fwww.learnopencv.com\u002F\n\n### 人工智能速查表：\n- 人工智能速查表精选：https:\u002F\u002Fbecominghuman.ai\u002Fcheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-science-pdf-f22dc900d2d7\n- 斯坦福CS229机器学习速查表：https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\n- 斯坦福CS230深度学习速查表：https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-230-deep-learning\n- 斯坦福CS221人工智能速查表：https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-221-artificial-intelligence\n- 人工智能速查表合集：http:\u002F\u002Fwww.aicheatsheets.com\u002F\n\n# 贡献指南：\n如果您认为需要添加内容或有任何建议，欢迎随时提交PR。如果您要添加内容，请确保您的提交信息格式为：**added \u003C资源名称> to \u003C章节名称>**\n\n##### 🌟 请给本仓库点个赞，让更多人看到并从中受益！\n\n*重要声明：所有产品名称、标识和品牌均为其各自所有者的财产。本仓库中使用的所有公司、产品和服务名称仅用于识别目的。使用这些名称、标识和品牌并不意味着对其表示认可或支持。*","# free-ai-resources 快速上手指南\n\n**项目简介**：`free-ai-resources` 并非一个需要安装运行的软件工具，而是一个由社区维护的**精选免费人工智能学习资源清单**。它汇集了全球顶尖高校（如斯坦福、哈佛）、科技巨头（如 Google、Microsoft、IBM）以及开源社区提供的免费课程、数学基础教程、机器学习路径和数据科学资料。本指南将帮助你快速浏览并利用这些资源开启 AI 学习之旅。\n\n## 环境准备\n\n由于本项目是资源索引列表，无需特定的系统环境或依赖库。你只需要：\n\n*   **硬件设备**：一台可以连接互联网的电脑、平板或手机。\n*   **网络环境**：\n    *   大部分资源托管在 YouTube、Coursera、edX 等国际平台。\n    *   **国内访问建议**：部分视频课程（如 Coursera、YouTube 链接）可能需要网络加速工具才能正常观看。\n    *   **替代方案**：对于编程类课程（如 Kaggle、W3Schools），通常可以直接访问；部分课程内容可在 Bilibili 等国内平台搜索对应的中文字幕搬运版。\n*   **基础知识**：建议具备基础的英语阅读能力（或使用浏览器翻译插件），以及基本的计算机操作技能。\n\n## 安装步骤\n\n本项目无需安装。你可以通过以下两种方式获取资源列表：\n\n1.  **直接在线浏览（推荐）**：\n    访问 GitHub 仓库页面直接查看分类整理的链接：\n    ```text\n    https:\u002F\u002Fgithub.com\u002Fmrsaeeddev\u002Ffree-ai-resources\n    ```\n\n2.  **克隆到本地（可选）**：\n    如果你希望离线查看或贡献内容，可以使用 Git 克隆仓库：\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002Fmrsaeeddev\u002Ffree-ai-resources.git\n    cd free-ai-resources\n    ```\n    然后在本地用 Markdown 阅读器或文本编辑器打开 `README.md` 文件。\n\n## 基本使用\n\n根据你的学习目标，直接在列表中点击对应链接即可开始学习。以下是针对不同阶段开发者的推荐路径：\n\n### 1. 零基础入门 (AI 概念与通识)\n如果你想了解什么是 AI 及其应用场景：\n*   **Elements of AI**: [https:\u002F\u002Fwww.elementsofai.com\u002F](https:\u002F\u002Fwww.elementsofai.com\u002F) (适合非技术背景，有中文版)\n*   **Google AI Education**: [https:\u002F\u002Fai.google\u002Feducation\u002F](https:\u002F\u002Fai.google\u002Feducation\u002F)\n*   **Crash Course AI (视频)**: [YouTube 链接](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GvYYFloV0aA&list=PL8dPuuaLjXtO65LeD2p4_Sb5XQ51par_b)\n\n### 2. 夯实数学基础 (线性代数、微积分、概率论)\nAI 的核心是数学，利用以下资源查漏补缺：\n*   **Khan Academy (可汗学院)**: [https:\u002F\u002Fwww.khanacademy.org\u002F](https:\u002F\u002Fwww.khanacademy.org\u002F) (全阶段数学基础)\n*   **3Blue1Brown (直观理解)**: [YouTube 频道](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCYO_jab_esuFRV4b17AJtAw) (强烈推荐观看其线性代数和微积分系列)\n*   **MIT OpenCourseWare**: [MIT 数学课程](http:\u002F\u002Focw.mit.edu\u002FOcwWeb\u002Fweb\u002Fcourses\u002Fcourses\u002Findex.htm#Mathematics)\n\n### 3. 机器学习实战 (核心技能)\n掌握经典机器学习算法与模型：\n*   **吴恩达 (Andrew NG) 机器学习**: [Coursera 课程](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning) (行业公认经典，建议寻找中文字幕版)\n*   **Google 机器学习速成课程**: [developers.google.com](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Fcrash-course)\n*   **Kaggle 学习路径**: \n    *   入门: [Intro to Machine Learning](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintro-to-machine-learning)\n    *   进阶: [Intermediate Machine Learning](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fintermediate-machine-learning)\n\n### 4. 深度学习与数据科学\n*   **Fast.ai**: [Practical Deep Learning for Coders](http:\u002F\u002Fcourse18.fast.ai\u002Fml) (自上而下的实战教学法)\n*   **IBM Data Science Professional Certificate**: [Coursera 专项课程](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fibm-data-science)\n\n> **提示**：列表中还包含了大量关于 LaTeX 排版、数学可视化工具（如 GeoGebra, Desmos）以及优质数学博客的链接，建议在需要特定工具或深入理论研究时按需查阅。","刚毕业的小张想转行成为 AI 工程师，但面对海量且分散的学习资料感到无从下手，急需一条清晰的入门路径。\n\n### 没有 free-ai-resources 时\n- **信息检索低效**：需要在谷歌、知乎、B 站等多个平台反复搜索\"AI 课程”，耗费数天筛选，却仍难以辨别课程质量与时效性。\n- **学习路径断裂**：找到的教程往往零散不成体系，缺乏从基础理论（如斯坦福课程）到实战应用（如机器人 AI）的连贯指引。\n- **资源成本高昂**：许多优质内容被锁定在付费墙后，或因不知道 IBM、Google 等大厂提供的免费官方入口而错失良机。\n- **行业动态滞后**：难以及时获取最新的 AI 研究论文、博客资讯及招聘需求，导致学习方向与市场脱节。\n\n### 使用 free-ai-resources 后\n- **一站式精准获取**：直接访问该仓库即可获取由社区精选的免费资源清单，涵盖 EdX、Udacity 及哈佛 CS50 等顶尖课程，瞬间节省大量搜索时间。\n- **结构化成长路线**：依托列表中分类清晰的课程（从原理到技术），快速构建起系统的知识框架，按图索骥完成进阶学习。\n- **零成本享受顶配资源**：轻松发现并利用微软 AI School、Google AI 教育等原本未知的高价值免费资源，极大降低试错成本。\n- **紧跟前沿与就业**：通过集成的研究论文、技术博客及职位链接，实时掌握行业脉搏，确保所学技能直接对标企业招聘需求。\n\nfree-ai-resources 将碎片化的全球优质 AI 资源整合为一张清晰的导航图，让每一位 aspiring AI Engineer 都能以最低成本开启高效的职业进阶之路。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fmrsaeeddev_free-ai-resources_8c7e60b2.png","mrsaeeddev","Saeed Ahmad","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fmrsaeeddev_01602015.png","I help developers to become better engineers!","Safepay","Pakistan","saeed_dev@yahoo.com","saeed.js.org","https:\u002F\u002Fgithub.com\u002Fmrsaeeddev",null,1813,319,"2026-04-13T17:42:09","MIT",1,"","未说明",{"notes":90,"python":88,"dependencies":91},"该项目并非一个需要安装运行的软件工具或代码库，而是一个 curated list（精选列表），主要包含免费的 AI、数学、机器学习和数据科学课程、教程、视频及博客文章的链接集合。因此，它没有特定的操作系统、GPU、内存、Python 版本或依赖库要求。用户只需使用浏览器访问列表中提供的各个外部链接即可学习相关资源。",[],[14,16,13,93,15],"其他",[95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112],"artificial-intelligence","artificial-neural-networks","machine-learning","machine-learning-algorithms","machinelearning","data-science","ai","research","data","data-science-projects","data-science-learning","deep-learning","deep-neural-networks","supervised-learning","unsupervised-learning","reinforcement-learning","hacktoberfest2020","hacktoberfest","2026-03-27T02:49:30.150509","2026-04-14T12:27:56.944540",[],[117],{"id":118,"version":119,"summary_zh":120,"released_at":121},247670,"v1.0","本次发布包括：\n\n✔️ 免费人工智能课程\n✔️ 免费机器学习课程\n✔️ 免费数据科学课程\n✔️ 免费深度学习课程\n✔️ 免费自然语言处理课程\n✔️ 免费计算机视觉课程\n✔️ 大公司的人工智能研究\n✔️ 开发者资源\n✔️ YouTube 频道\n\n","2020-08-01T07:49:42"]