{"data":{"slug":"aimi-cn-ailearners","name":"AILearners","tagline":"AI learning resources including tutorials and notes on ML, DL, NLP, and CV","github_url":"https://github.com/aimi-cn/AILearners","owner":"aimi-cn","repo":"AILearners","owner_avatar_url":"https://avatars.githubusercontent.com/u/49583543?v=4","primary_language":"Python","stars":706,"forks":149,"topics":["ai","computer-vision","cs231n","cv","deep-learning","dl","jianzhioffer","machine-learning","ml","nlp"],"archived":false,"github_pushed_at":"2020-11-14T06:54:02+00:00","maintenance_label":"Dormant","stars_delta_30d":6,"url":"https://www.graphcanon.com/tools/aimi-cn-ailearners","markdown_url":"https://www.graphcanon.com/tools/aimi-cn-ailearners.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/aimi-cn-ailearners","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=aimi-cn-ailearners","description":"机器学习、深度学习、自然语言处理、计算机视觉、各种算法等AI领域相关技术的路线、教程、干货分享。笔记有：机器学习实战、剑指Offer、cs231n、cs131、吴恩达机器学习、cs224n、python自然语言处理实战","homepage_url":"https://github.com/aimi-cn/AILearners","license":"Apache-2.0","open_issues":1,"watchers":14,"ai_summary":"A collection of AI educational materials covering machine learning, deep learning, natural language processing (NLP), and computer vision with detailed course notes and tutorial materials.","readme_excerpt":"#

AIMI-CN 推荐AI学习路线以及课程笔记

\n\n\n## 组织介绍：\n\n我们是一群爱好AI学习的爱好者！在这里我们一起学习、互相督促、一起装逼~ \n我们更新的一些AI相关的笔记~ 包括算法、机器学习、深度学习、自然语言处理，之后也会更新更多的笔记让大家一起学习~\n\n**AIMI-CN AI学习交流群(里面有各种AI相关的资源)【1015286623】\"AI~AIMICN\"**\n\n**我们的公众号也会时不时的推送各种干货等你来关注~**\n\n**搜索微信公众号：'AI-ming3526'或者'计算机视觉这件小事' 获取更多AI方向机器学习资源干货**\n\n
\n\n# 我们的学习笔记(涉及算法、机器学习、深度学习、NLP等方向)\n\n## 一、算法方向\n\n## 1、算法与人生 [@bidongqinxian](https://github.com/bidongqinxian)\n\n为你学习机器学习开头指引明路~\n\n[详细文章地址](https://github.com/aimi-cn/AILearners/tree/master/blog/Algorithm)\n\n[源码地址](https://github.com/aimi-cn/AILearners/tree/master/blog/src/py2.x/algorithm)\n\n## 2、剑指Offer算法习题详细解析 [@xiaoming3526](https://github.com/xiaoming3526)\n\n牛客网上刷《剑指Offer》的刷题笔记，旨在提升我们算法能力~\n\n[详细文章地址](https://github.com/aimi-cn/AILearners/tree/master/blog/Algorithm/jianzhi_offer)\n\n[csdn地址](https://blog.csdn.net/baidu_31657889/article/category/9059648)\n\n## 二、机器学习方向\n\n## 1、吴恩达老师机器学习笔记[@JermaineZ](https://github.com/JermaineZ) [@niuhongying](https://github.com/niuhongying) [@liuxinyi222](https://github.com/liuxinyi222)\n为你打开机器学习的大门~ \n\n[**详细的笔记地址**](https://github.com/aimi-cn/AILearners/tree/master/blog/ml/ml_wnd)\n\n## 2、《机器学习实战》课程及代码详细解析、书籍、数据集下载 [@Fermin](https://github.com/xiaoming3526)\n\n让你用代码来体验机器学习~\n\n[**课程详细笔记地址**](https://github.com/aimi-cn/AILearners/tree/master/blog/ml/jqxxsz)\n\n[机器学习实战书籍](https://github.com/aimi-cn/AILearners/tree/master/books)\n\n[源码和数据集下载](https://github.com/aimi-cn/AILearners/tree/master/data/ml/jqxxsz/sourceData)\n\n\n## 3、《机器学习》周志华西瓜书笔记[待定]()\n\n## 三、深度学习方向\n\n## 斯坦福cs231n 面向视觉识别的卷积神经网络课程笔记[@5people]()\n\n详细介绍什么是神经网络、CNN、RNN、GAN~\n\n[**详细的笔记地址**](https://github.com/aimi-cn/AILearners/tree/master/blog/dl/cs231n)\n\n## 四、自然语言处理NLP方向\n\n## 1、斯坦福cs224n **2019** 基于深度学习的NLP 课程笔记、课后作业、学习资料 [@Guokaijie](https://github.com/abc907558136) \n\n我们一起学习最前沿的NLP知识~\n\n[cs224n课程详细笔记](https://github.com/aimi-cn/AILearners/tree/master/blog/nlp/cs224n) \n\n[cs224n学习资料](https://pan.baidu.com/s/1ADcGT4sljV1lYPWNhFXs2Q )\t 提取码：e234 \n\n[youtube视频链接](https://www.youtube.com/watch?v=8rXD5-xhemo&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=1)\t国内可以在B站找到\n\n## 2、python自然语言处理实战书籍代码笔记[@changan](https://github.com/stuchangan)\n\n在实战代码中学习NLP~\n\n[详细笔记地址](https://github.com/aimi-cn/AILearners/tree/master/blog/nlp/NLP-Core%20Technology%20And%20Algorithm%20With%20Python) \n\n[代码以及书籍下载地址](https://github.com/aimi-cn/AILearners/tree/master/data/nlp/NLP-Core%20Technology%20And%20Algorithm%20With%20Python)\n\n\n# AIMI-CN推荐AI学习路线：机器学习、深度学习、NLP等推荐的学习资料以及学习方向\n\n观看先决条件： \n**选择，方法，坚持** \n我们都知道现在资源是非常非常的多 我们首先选择一份真正适合自己的资料，然后用适合自己的方法来学习~最后最重要的就是坚持！！！\n\n转载一个非常牛的几个组织整理出来的AI学习路线 [重磅 | 完备的 AI 学习路线，最详细的资源整理！](https://github.com/apachecn/ai-roadmap/blob/master/ai-union-201904/README.md)\n\n# 一、机器学习\n\n## 机器学习建议\n\n学习机器学习需要一定的数学基础，但是仅仅是一点数学基础，不要被这些吓坏了，各位都是大佬，拿起键盘就是干。\n\n我就根据自己的一点点经验来分析一下应该怎么学----\n\n首先需要的是两个放弃：\n\n## 1. 放弃海量资料！\n\n没错，就是放弃海量资料！在我们想要入门机器学习的时候，往往会搜集很多资料，什么 xx学院机器学习内部资源、机器学习从入门到进阶百 G 资源、xx 人工智能教程，等等。很多时候我们拿着十几 G、几百 G 的学习资源，然后踏踏实实地放到了某云盘里存着，等着日后慢慢学习。殊不知，有 90% 的人仅仅只是搜集资料、保存资料而已，放在云盘里一年半载也忘了打开学习。躺在云盘的资料很多时候只是大多数人“以后好好学习”的自我安慰和“自我”安全感而已。而且，面对海量的学习资料，很容易陷入到一种迷茫的状态，最直接的感觉就是：天啊，有这么多东西要学！天啊，还有这么多东西没学！简单来说，就是选择越多，越容易让人陷入无从选择的困境。\n\n所以，第一步就是要放弃海量资料！而是选择一份真正适合自己的资料，好好研读下去！\n\n## 2. 放弃从零起步！\n\n说到入门，很多人会想着那就要从最基础的知识开始学起！机器学习是一门融合概率论、线性代数、凸优化、计算机、神经科学等多方面的复杂技术。学好机器学习需要的理论知识很多，有些人可能基础不是特别扎实，就想着从最底层的知识开始学起，概率论、线性代数、机器学习凸优化公式推导，等等。但是这样做的坏处是比较耗时间，而且容易造成“懈怠学习”，打消学习的积极性。因为啃书本和推导公式相对来说是比较枯燥的，远不如自己搭建一个简单的回归模型更能激发自己的学习积极性。当然，不是说不需要钻研基础知识，基础理论知识非常重要！只是说，在入门的时候，最好先从顶层框架上有个系统的认识，然后再从实践到理论，有的放矢的查缺补漏机器学习知识点。从宏观到微观，从整体到细节，更有利于机器学习快速入门！而且从学习的积极性来说，也起到了“正反馈”的作用。\n\n## 3、机器学习入门学习路线\n\n好了，谈完了机器学习入门之前的两个“放弃”之后，我们就在介绍一下入门路线。\n\n### 3.1 数学基础\n\n个人认为首先需要的数学基础：概率论、矩阵论以及微积分。没有也不要紧，边看边学，看到不会的查一下就行了。\n\n> 【免费】数学教学视频 - 可汗学院 入门篇\n\n| 概率","github_created_at":"2019-04-13T08:36:05+00:00","created_at":"2026-07-11T12:28:18.040925+00:00","updated_at":"2026-09-23T06:00:39.134294+00:00","categories":[{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"},{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"cs231n","name":"cs231n"},{"slug":"cv","name":"cv"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"dl","name":"dl"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"ml","name":"ml"}],"trust":{"provenance":{"is_fork":false,"github_id":181143453,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-23T06:00:38.334Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":2138,"last_release_at":null,"stars_delta_30d":6,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T12:28:19.696Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-23T06:00:38.806Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-23T06:00:38.806Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-23T06:00:38.806Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need a structured learning path with focus on both theoretical and practical understanding of AI fields.","Suitable if you want to engage deeply with specific courses like cs231n or cs224n, and have access to their detailed notes."],"when_not_to_use":["Avoid if you prefer live interactions over self-paced learning resources.","Not the best fit if your primary interest is in cutting-edge research papers rather than comprehensive course materials."],"source":"enrich:decision_facts","observed_at":"2026-07-17T04:06:22.302Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"AILearners offers detailed tutorials and notes on AI subjects like ML, DL, NLP, and CV, emphasizing self-help study paths through course materials and practical exercises in Python."}]}}