[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9824":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":16,"stars30d":17,"stars90d":15,"forks30d":15,"starsTrendScore":18,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":22,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":15,"starSnapshotCount":15,"syncStatus":16,"lastSyncTime":31,"discoverSource":32},9824,"MachineLearning","allmachinelearning\u002FMachineLearning","allmachinelearning","Machine learning resources","https:\u002F\u002Fallmachinelearning.github.io\u002FMachineLearning\u002F",null,3814,978,301,3,0,2,21,6,65.07,false,"master",true,[24,25,26,27],"artificial-intelligence","datamining","deep-learning","machinelearning","2026-06-12 04:00:46","# 机器学习资源 Machine learning Resources\r\n\r\n**致力于分享最新最全面的机器学习资料，欢迎你成为贡献者!**\r\n\r\n*快速开始学习：* \r\n\r\n- 周志华的[《机器学习》](https:\u002F\u002Fpan.baidu.com\u002Fs\u002F1hscnaQC)作为通读教材，不用深入，从宏观上了解机器学习\r\n  - 《机器学习》西瓜书公式推导解析：https:\u002F\u002Fdatawhalechina.github.io\u002Fpumpkin-book\u002F\r\n\r\n- 最新的[《神经网络与深度学习》](https:\u002F\u002Fmp.weixin.qq.com\u002Fs?__biz=MzIwOTc2MTUyMg==&mid=2247488439&idx=1&sn=df51b67ac2a42fe1a8417a7e4d308b8b&chksm=976fb62aa0183f3c8cfbfcf2c1613aa3a168f782bc5b439aa2a5db9574a33f678a081a1d24a5&mpshare=1&scene=1&srcid=0409hgaWjfxz2LzGtniTpAKh&key=12a4c5f4665589b6914fa6a60a7fe4bd6a4fc4855ac8967b945678646a60c26482467697a46b85e85c7a6a7d564aac41d6c0312307a7f95ba299d3b3cf8433f9a159f999d9484534452672dbdd9fd270&ascene=1&uin=NjMzMjQzMTYw&devicetype=Windows+10&version=62060739&lang=zh_CN&pass_ticket=CIhr0hAvTnkZIvwFNRQ2%2BWhir8OVCkCt9tarvfIPS5SWtyyQKMLGOBt%2BItSffrll)\r\n\r\n- 李航的[《统计学习方法》](https:\u002F\u002Fpan.baidu.com\u002Fs\u002F1dF2b4jf)作为经典的深入案例，仔细研究几个算法的来龙去脉 | [书中的代码实现](https:\u002F\u002Fgithub.com\u002FWenDesi\u002Flihang_book_algorithm)\r\n\r\n- 使用Python语言，根据[《机器学习实战》](https:\u002F\u002Fpan.baidu.com\u002Fs\u002F1gfzV7PL)快速上手写程序\r\n    \r\n- 来自国立台湾大学李宏毅老师的机器学习和深度学习中文课程，强烈推荐：[课程](http:\u002F\u002Fspeech.ee.ntu.edu.tw\u002F~tlkagk\u002Fcourses.html)\r\n\r\n- 《迁移学习导论》助你快速入门迁移学习！ [书的主页](http:\u002F\u002Fjd92.wang\u002Ftlbook)\r\n  - 迁移学习统一代码库：[Domain adaptation](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Ftransferlearning\u002Ftree\u002Fmaster\u002Fcode\u002FDeepDA) | [Domain generalization](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Ftransferlearning\u002Ftree\u002Fmaster\u002Fcode\u002FDeepDG) | [更多代码](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Ftransferlearning)\r\n\r\n- 最后，你可能想真正实战一下。那么，请到著名的机器学习竞赛平台Kaggle上做一下这些基础入门的[题目](https:\u002F\u002Fwww.kaggle.com\u002Fcompetitions?sortBy=deadline&group=all&page=1&pageSize=20&segment=gettingStarted)吧！(Kaggle上对于每个问题你都可以看到别人的代码，方便你更加快速地学习)  [Kaggle介绍及入门解读](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F25686876) [可以用来练手的数据集](https:\u002F\u002Fwww.kaggle.com\u002Fannavictoria\u002Fml-friendly-public-datasets\u002Fnotebook)\r\n\r\n其他有用的资料：\r\n\r\n- 想看别人怎么写代码？[机器学习经典教材《PRML》所有代码实现](https:\u002F\u002Fgithub.com\u002Fctgk\u002FPRML)\r\n\r\n- [机器学习算法Python实现](https:\u002F\u002Fgithub.com\u002Flawlite19\u002FMachineLearning_Python)\r\n\r\n- [吴恩达新书：Machine Learning Yearning中文版](https:\u002F\u002Fpan.baidu.com\u002Fs\u002F10kosKx6rDguS4tPejY-fRw)\r\n\r\n- 另外，对于一些基础的数学知识，你看[深度学习(花书)中文版](https:\u002F\u002Fgithub.com\u002Fexacity\u002Fdeeplearningbook-chinese)就够了。这本书同时也是**深度学习**经典之书。\r\n\r\n- 来自南京大学周志华小组的博士生写的一本小而精的[解析卷积神经网络—深度学习实践手册](http:\u002F\u002Flamda.nju.edu.cn\u002Fweixs\u002Fbook\u002FCNN_book.html)\r\n\r\n- - -\r\n\r\n[一个简洁明了的时间序列处理(分窗、特征提取、分类)库：Seglearn](https:\u002F\u002Fdmbee.github.io\u002Fseglearn\u002Findex.html)\r\n\r\n[计算机视觉这一年：这是最全的一份CV技术报告](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F31430602)\r\n\r\n[深度学习(花书)中文版](https:\u002F\u002Fgithub.com\u002Fexacity\u002Fdeeplearningbook-chinese)\r\n\r\n**[深度学习最值得看的论文](http:\u002F\u002Fwww.dlworld.cn\u002FYeJieDongTai\u002F4385.html)**\r\n\r\n**[最全面的深度学习自学资源集锦](http:\u002F\u002Fdataunion.org\u002F29975.html)**\r\n\r\n**[Machine learning surveys](https:\u002F\u002Fgithub.com\u002Fmetrofun\u002Fmachine-learning-surveys\u002F)**\r\n\r\n**[快速入门TensorFlow](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples)**\r\n\r\n[自然语言处理数据集](http:\u002F\u002Fabunchofdata.com\u002Fdatasets-for-natural-language-processing\u002F)\r\n \r\n[Learning Machine Learning? Six articles you don’t want to miss](http:\u002F\u002Fwww.ibmbigdatahub.com\u002Fblog\u002Flearning-machine-learning-six-articles-you-don-t-want-miss)\r\n\r\n[Getting started with machine learning documented by github](https:\u002F\u002Fgithub.com\u002Fcollections\u002Fmachine-learning)\r\n\r\n- - -\r\n\r\n\r\n## 研究领域资源细分\r\n\r\n- ### [深度学习 Deep learning](https:\u002F\u002Fgithub.com\u002FChristosChristofidis\u002Fawesome-deep-learning)\r\n\r\n- ### [强化学习 Reinforcement learning](https:\u002F\u002Fgithub.com\u002Faikorea\u002Fawesome-rl)\r\n\r\n- ### [迁移学习 Transfer learning](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Ftransferlearning)\r\n\r\n- ### [分布式学习系统 Distributed learning system](https:\u002F\u002Fgithub.com\u002Ftheanalyst\u002Fawesome-distributed-systems)\r\n\r\n- ### [计算机视觉\u002F机器视觉 Computer vision \u002F machine vision](https:\u002F\u002Fgithub.com\u002Fjbhuang0604\u002Fawesome-computer-vision)\r\n\r\n- ### [自然语言处理 Natural language procesing](https:\u002F\u002Fgithub.com\u002FNativeatom\u002FNaturalLanguageProcessing)\r\n\r\n- ### [生物信息学 Bioinfomatics](https:\u002F\u002Fgithub.com\u002Fdanielecook\u002FAwesome-Bioinformatics)\r\n\r\n- ### [行为识别 Activity recognition](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002Factivityrecognition)\r\n\r\n- ### [多智能体 Multi-Agent](http:\u002F\u002Fddl.escience.cn\u002Ff\u002FILKI)\r\n\r\n- - -\r\n\r\n##  开始学习：预备知识 Prerequisite\r\n\r\n- [学习知识与路线图](https:\u002F\u002Fmetacademy.org\u002F)\r\n\r\n- [MIT线性代数课堂笔记(中文)](https:\u002F\u002Fgithub.com\u002Fzlotus\u002Fnotes-linear-algebra)\r\n\r\n- [概率与统计 The Probability and Statistics Cookbook](http:\u002F\u002Fstatistics.zone\u002F)\r\n\r\n- Python\r\n\r\n    - [Learn X in Y minutes](https:\u002F\u002Flearnxinyminutes.com\u002Fdocs\u002Fpython\u002F)\r\n\r\n    - [Python机器学习互动教程](https:\u002F\u002Fwww.springboard.com\u002Flearning-paths\u002Fmachine-learning-python\u002F)\r\n\r\n- Markdown\r\n\r\n    - [Mastering Markdown](https:\u002F\u002Fguides.github.com\u002Ffeatures\u002Fmastering-markdown\u002F) - Markdown is a easy-to-use writing tool on the GitHu.\r\n\r\n- R\r\n\r\n    - [R Tutorial](http:\u002F\u002Fwww.cyclismo.org\u002Ftutorial\u002FR\u002F)\r\n\r\n- Python和Matlab的一些cheat sheet：http:\u002F\u002Fddl.escience.cn\u002Ff\u002FIDkq 包含：\r\n\r\n    - Numpy、Scipy、Pandas科学计算库\r\n\r\n    - Matlab科学计算\r\n\r\n    - Matplotlib画图\r\n\r\n- 深度学习框架\r\n\r\n    - Python\r\n        - [TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002F)\r\n        - [Scikit-learn](http:\u002F\u002Fscikit-learn.org\u002F)\r\n        - [PyTorch](http:\u002F\u002Fpytorch.org\u002F)\r\n        - [Keras](https:\u002F\u002Fkeras.io\u002F)\r\n        - [MXNet](http:\u002F\u002Fmxnet.io\u002F)|[相关资源大列表](https:\u002F\u002Fgithub.com\u002Fchinakook\u002FAwesome-MXNet)\r\n        - [Caffe](http:\u002F\u002Fcaffe.berkeleyvision.org\u002F)\r\n        - [Caffe2](https:\u002F\u002Fcaffe2.ai\u002F)\r\n\r\n    - Java\r\n        - [Deeplearning4j](https:\u002F\u002Fdeeplearning4j.org\u002F)\r\n\r\n    - Matlab\r\n        - [Neural Network Toolbox](https:\u002F\u002Fcn.mathworks.com\u002Fhelp\u002Fnnet\u002Findex.html)\r\n        - [Deep Learning Toolbox](https:\u002F\u002Fcn.mathworks.com\u002Fmatlabcentral\u002Ffileexchange\u002F38310-deep-learning-toolbox)\r\n\r\n- - -\r\n\r\n\r\n## 文档 notes\r\n\r\n- [综述文章汇总](https:\u002F\u002Fgithub.com\u002Fallmachinelearning\u002FMachineLearning\u002Fblob\u002Fmaster\u002Fnotes\u002Fsurvey_readme.md)\r\n\r\n- [近200篇机器学习资料汇总！](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F26136757)\r\n\r\n- [机器学习入门资料](https:\u002F\u002Fgithub.com\u002Fallmachinelearning\u002FMachineLearning\u002Fblob\u002Fmaster\u002Fnotes\u002FMLMaterials.md)\r\n\r\n- [MIT.Introduction to Machine Learning](http:\u002F\u002Fddl.escience.cn\u002Ff\u002FIwtu)\r\n\r\n- [东京大学同学做的人机交互报告](https:\u002F\u002Fgithub.com\u002Fallmachinelearning\u002FMachineLearning\u002Fblob\u002Fmaster\u002Fnotes\u002FFieldResearchinChina927-104.pdf)\r\n\r\n- [人机交互简介](https:\u002F\u002Fgithub.com\u002Fjindongwang\u002FHCI)\r\n\r\n- [人机交互与创业论坛](https:\u002F\u002Fgithub.com\u002Fallmachinelearning\u002FMachineLearning\u002Fblob\u002Fmaster\u002Fnotes\u002F%E4%BA%BA%E6%9C%BA%E4%BA%A4%E4%BA%92%E4%B8%8E%E5%88%9B%E4%B8%9A%E8%AE%BA%E5%9D%9B.md)\r\n\r\n- [职场机器学习入门](https:\u002F\u002Fgithub.com\u002Fallmachinelearning\u002FMachineLearning\u002Fblob\u002Fmaster\u002Fnotes\u002F%E8%81%8C%E5%9C%BA-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E5%85%A5%E9%97%A8.md)\r\n\r\n- [机器学习的发展历程及启示](http:\u002F\u002Fmt.sohu.com\u002F20170326\u002Fn484898474.shtml), (@Prof. Zhihua Zhang\u002F@张志华教授)\r\n\r\n- [常用的距离和相似度度量](https:\u002F\u002Fgithub.com\u002Fallmachinelearning\u002FMachineLearning\u002Fblob\u002Fmaster\u002Fnotes\u002Fdistance%20and%20similarity.md)\r\n\r\n- - -\r\n\r\n\r\n## 课程与讲座 Course and talk\r\n\r\n### 机器学习 Machine Learning\r\n \r\n[台湾大学应用深度学习课程](https:\u002F\u002Fwww.csie.ntu.edu.tw\u002F~yvchen\u002Ff106-adl\u002Findex.html)\r\n\r\n- [神经网络，机器学习，算法，人工智能等 30 门免费课程详细清单](http:\u002F\u002Fwww.datasciencecentral.com\u002Fprofiles\u002Fblogs\u002Fneural-networks-for-machine-learning)\r\n  \r\n- [斯坦福机器学习入门课程](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning)，讲师为Andrew Ng，适合数学基础一般的人，适合入门，但是学完会发现只是懂个大概，也就相当于什么都不懂。省略了很多机器学习的细节\r\n\r\n- [Neural Networks for Machine Learning](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fneural-networks), Coursera上的著名课程，由Geoffrey Hinton教授主讲。\r\n\r\n- [Stanford CS 229](http:\u002F\u002Fcs229.stanford.edu\u002Fmaterials.html), Andrew Ng机器学习课无阉割版，Notes比较详细，可以对照学习[CS229课程讲义的中文翻译](https:\u002F\u002Fgithub.com\u002FKivy-CN\u002FStanford-CS-229-CN)。\r\n\r\n- [CMU 10-702 Statistical Machine Learning](http:\u002F\u002Fwww.stat.cmu.edu\u002F~larry\u002F=sml\u002F), 讲师是Larry Wasserman，应该是统计系开的机器学习，非常数学化，第一节课就提到了RKHS(Reproducing Kernel Hilbert Space),建议数学出身的同学看或者是学过实变函数泛函分析的人看一看\r\n\r\n- [CMU 10-715 Advanced Introduction to Machine Learning](https:\u002F\u002Fwww.cs.cmu.edu\u002F~epxing\u002FClass\u002F10715\u002F)，同样是CMU phd级别的课，节奏快难度高\r\n\r\n- [机器学习基石](https:\u002F\u002Fwww.coursera.org\u002Fcourse\u002Fntumlone)（适合入门）。国立台湾大学[林轩田](https:\u002F\u002Fwww.coursera.org\u002Finstructor\u002Fhtlin)\r\n\r\n- [机器学习技法](https:\u002F\u002Fwww.coursera.org\u002Fcourse\u002Fntumltwo)（适合提高）。国立台湾大学[林轩田](https:\u002F\u002Fwww.coursera.org\u002Finstructor\u002Fhtlin)\r\n\r\n- [Machine Learning for Data Analysis](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning-data-analysis), Coursera上Wesleyan大学的Data Analysis and Interpretation专项课程第四课。\r\n\r\n- Max Planck Institute for Intelligent Systems Tübingen[德国马普所智能系统研究所2013的机器学习暑期学校视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E),仔细翻这个频道还可以找到2015的暑期学校视频\r\n\r\n- 知乎Live：[我们一起开始机器学习吧](https:\u002F\u002Fwww.zhihu.com\u002Flives\u002F792423196996546560)，[机器学习入门之特征工程](https:\u002F\u002Fwww.zhihu.com\u002Flives\u002F819543866939174912)\r\n\r\n### 深度学习 Machine Learning\r\n\r\n- 斯坦福大学Feifei Li教授的[CS231n系列深度学习课程](http:\u002F\u002Fcs231n.stanford.edu\u002F)。Feifei Li目前是Google的科学家，深度学习与图像识别方面的大牛。这门课的笔记可以看[这里](https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F21930884)。\r\n\r\n- [CS224n: Natural Language Processing](http:\u002F\u002Fcs224n.stanford.edu). Course instructors: Chris Manning, Richard Socher.\r\n\r\n### 强化学习 Machine Learning\r\n\r\n- [CS 294 Deep Reinforcement Learning, Fall 2017](http:\u002F\u002Frll.berkeley.edu\u002Fdeeprlcourse\u002F). Course instructors: Sergey Levine, John Schulman, Chelsea Finn.\r\n\r\n- [UCL Course on RL](http:\u002F\u002Fwww0.cs.ucl.ac.uk\u002Fstaff\u002Fd.silver\u002Fweb\u002FTeaching.html)\r\n\r\n- [CS234: Reinforcement Learning](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs234\u002Findex.html). 暂无视频\r\n\r\n- - -\r\n\r\n\r\n## 相关书籍 reference book\r\n\r\n- [Hands on Machine Learning with Scikit-learn and Tensorflow](https:\u002F\u002Fmy.pcloud.com\u002Fpublink\u002Fshow?code=XZ9ev77Zk2l6xcMtfIhHm7mRKAYhISb6sl3k)\r\n\r\n- 入门读物 [The Elements of Statistical Learning(英文第二版),The Elements of Statistical Learning.pdf](http:\u002F\u002Fddl.escience.cn\u002Fff\u002FemZH)\r\n\r\n- [机器学习](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F26708119\u002F), (@Prof. Zhihua Zhou\u002F周志华教授)\r\n\r\n- [统计学习方法](https:\u002F\u002Fbook.douban.com\u002Fsubject\u002F10590856\u002F), (@Dr. Hang Li\u002F李航博士)\r\n\r\n- [一些Kindle读物](http:\u002F\u002Fddl.escience.cn\u002Ff\u002FIwWE):\r\n\r\n\t- 利用Python进行数据分析\r\n\r\n\t- 跟老齐学Python：从入门到精通\r\n\r\n\t- Python与数据挖掘 (大数据技术丛书) - 张良均\r\n\r\n\t- Python学习手册\r\n\r\n\t- Python性能分析与优化\r\n\r\n\t- Python数据挖掘入门与实践\r\n\r\n\t- Python数据分析与挖掘实战(大数据技术丛书) - 张良均\r\n\r\n\t- Python科学计算(第2版)\r\n\r\n\t- Python计算机视觉编程 [美] Jan Erik Solem\r\n\r\n\t- python核心编程(第三版)\r\n\r\n\t- Python核心编程（第二版）\r\n\r\n\t- Python高手之路 - [法] 朱利安·丹乔（Julien Danjou）\r\n\r\n\t- Python编程快速上手 让繁琐工作自动化\r\n\r\n\t- Python编程：从入门到实践\r\n\r\n\t- Python3 CookBook中文版\r\n\r\n\t- 终极算法机器学习和人工智能如何重塑世界 - [美 ]佩德罗·多明戈斯\r\n\r\n\t- 机器学习系统设计 (图灵程序设计丛书) - [美]Willi Richert & Luis Pedro Coelho\r\n\r\n\t- 机器学习实践指南：案例应用解析（第2版） (大数据技术丛书) - 麦好\r\n\r\n\t- 机器学习实践 测试驱动的开发方法 (图灵程序设计丛书) - [美] 柯克（Matthew Kirk）\r\n\r\n\t- 机器学习：实用案例解析\r\n  \r\n\r\n- [数学](https:\u002F\u002Fmega.nz\u002F#F!WVAlGL6B!mqIjYoTjiQnO4jBGVLRIWA\r\n):\r\n\r\n    - Algebra - Michael Artin\r\n\r\n    - Algebra - Serge Lang\r\n\r\n    - Basic Topology - M.A. Armstrong\r\n\r\n    - Convex Optimization by Stephen Boyd & Lieven Vandenberghe\r\n\r\n    - Functional Analysis by Walter Rudin\r\n\r\n    - Functional Analysis, Sobolev Spaces and Partial Differential Equations by Haim Brezis\r\n\r\n    - Graph Theory - J.A. Bondy, U.S.R. Murty\r\n\r\n    - Graph Theory - Reinhard Diestel\r\n\r\n    - Inside Interesting Integrals - Pual J. Nahin\r\n\r\n    - Linear Algebra and Its Applications - Gilbert Strang\r\n\r\n    - Linear and Nonlinear Functional Analysis with Applications - Philippe G. Ciarlet\r\n\r\n    - Mathematical Analysis I - Vladimir A. Zorich\r\n\r\n    - Mathematical Analysis II - Vladimir A. Zorich\r\n\r\n    - Mathematics for Computer Science - Eric Lehman, F Thomson Leighton, Alber R Meyer\r\n\r\n    - Matrix Cookbook, The - Kaare Brandt Petersen, Michael Syskind Pedersen\r\n\r\n    - Measures, Integrals and Martingales - René L. Schilling\r\n\r\n    - Principles of Mathematical Analysis - Walter Rudin\r\n\r\n    - Probabilistic Graphical Models: Principles and Techniques - Daphne Koller, Nir Friedman\r\n\r\n    - Probability: Theory and Examples - Rick Durrett\r\n\r\n    - Real and Complex Analysis - Walter Rudin\r\n\r\n    - Thomas' Calculus - George B. Thomas\r\n\r\n    - 普林斯顿微积分读本 - Adrian Banner\r\n\r\n\r\n- [Packt每日限免电子书精选](http:\u002F\u002Fddl.escience.cn\u002Ff\u002FIS4a):\r\n\r\n\t- Learning Data Mining with Python\r\n\r\n\t- Matplotlib for python developers\r\n\r\n\t- Machine Learing with Spark\r\n\r\n\t- Mastering R for Quantitative Finance\r\n\r\n\t- Mastering matplotlib\r\n\r\n\t- Neural Network Programming with Java\r\n\r\n\t- Python Machine Learning\r\n\r\n\t- R Data Visualization Cookbook\r\n\r\n\t- R Deep Learning Essentials\r\n\r\n\t- R Graphs Cookbook second edition\r\n\r\n\t- D3.js By Example \r\n\r\n\t- Data Analysis With R\r\n\r\n\t- Java Deep Learning Essentials\r\n\r\n\t- Learning Bayesian Models with R\r\n\r\n\t- Learning Pandas\r\n\r\n\t- Python Parallel Programming Cookbook\r\n\r\n\t- Machine Learning with R\r\n\r\n---\r\n\r\n\r\n## 其他 Miscellaneous\r\n\r\n- [机器学习日报](http:\u002F\u002Fforum.ai100.com.cn\u002F)：每天更新学术和工业界最新的研究成果\r\n\r\n- [机器之心](https:\u002F\u002Fwww.jiqizhixin.com\u002F)\r\n\r\n- [集智社区](https:\u002F\u002Fjizhi.im\u002Findex)\r\n\r\n- - -\r\n\r\n\r\n## 如何加入 How to contribute\r\n\r\n如果你对本项目感兴趣，非常欢迎你加入！\r\n\r\n- 正常参与：请直接fork、pull都可以\r\n- 如果要上传文件：请**不要**直接上传到项目中，否则会造成git版本库过大。正确的方法是上传它的**超链接**。如果你要上传的文件本身就在网络中（如paper都会有链接），直接上传即可；如果是自己想分享的一些文件、数据等，鉴于国内网盘的情况，请按照如下方式上传：\r\n\t- (墙内)目前没有找到比较好的方式，只能通过链接，或者自己网盘的链接来做。\r\n\t- (墙外)首先在[UPLOAD](https:\u002F\u002Fmy.pcloud.com\u002F#page=puplink&code=4e9Z0Vwpmfzvx0y2OqTTTMzkrRUz8q9V)直接上传（**不**需要注册账号）；上传成功后，在[DOWNLOAD](https:\u002F\u002Fmy.pcloud.com\u002Fpublink\u002Fshow?code=kZWtboZbDDVguCHGV49QkmlLliNPJRMHrFX)里找到你刚上传的文件，共享链接即可。\r\n\r\n\r\n\r\n## 如何开始项目协同合作\r\n\r\n[快速了解github协同工作](http:\u002F\u002Fhucaihua.cn\u002F2016\u002F12\u002F02\u002Fgithub_cooperation\u002F)\r\n\r\n[及时更新fork项目](https:\u002F\u002Fjinlong.github.io\u002F2015\u002F10\u002F12\u002Fsyncing-a-fork\u002F)\r\n\r\n\r\n#### [贡献者 Contributors](https:\u002F\u002Fgithub.com\u002Fallmachinelearning\u002FMachineLearning\u002Fblob\u002Fmaster\u002Fcontributors.md)\r\n\r\n\r\n\r\n\r\n\r\n","该项目是一个机器学习资源库，旨在为学习者提供最新最全面的机器学习资料。它汇集了多种经典教材、在线课程、代码实现以及竞赛平台链接，涵盖了从基础入门到进阶实战的各个阶段。项目中推荐了多本知名书籍如《机器学习》（周志华）、《统计学习方法》（李航）等，并提供了这些书籍相关的公式推导解析和代码实现。此外，还收录了来自台湾大学李宏毅教授的中文授课视频以及Kaggle竞赛平台的实战题目。此资源库适合任何希望系统性地学习或提升自己在机器学习领域知识与技能的人士使用。","2026-06-11 03:24:54","top_topic"]