Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.
Awesome Lists | Featured Topics | Projects
https://github.com/allmachinelearning/MachineLearning

Machine learning resources
https://github.com/allmachinelearning/MachineLearning
artificial-intelligence datamining deep-learning machinelearning
Last synced: 13 days ago
JSON representation
Machine learning resources
Host: GitHub
URL: https://github.com/allmachinelearning/MachineLearning
Owner: allmachinelearning
Created: 2017-05-09T10:29:21.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2024-04-01T10:34:07.000Z (7 months ago)
Last Synced: 2024-10-23T07:35:06.780Z (20 days ago)
Topics: artificial-intelligence, datamining, deep-learning, machinelearning
Homepage: https://allmachinelearning.github.io/MachineLearning/
Size: 301 KB
Stars: 3,452
Watchers: 303
Forks: 961
Open Issues: 4
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

awesome - MachineLearning - Machine learning resources (Others)
personal-awesome-list - MachineLearning
README

        # 机器学习资源 Machine learning Resources

**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!**

*快速开始学习：* 

- 周志华的[《机器学习》](https://pan.baidu.com/s/1hscnaQC)作为通读教材，不用深入，从宏观上了解机器学习

  - 《机器学习》西瓜书公式推导解析：https://datawhalechina.github.io/pumpkin-book/

- 最新的[《神经网络与深度学习》](https://mp.weixin.qq.com/s?__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)

- 李航的[《统计学习方法》](https://pan.baidu.com/s/1dF2b4jf)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://github.com/WenDesi/lihang_book_algorithm)

- 使用Python语言，根据[《机器学习实战》](https://pan.baidu.com/s/1gfzV7PL)快速上手写程序

    

- 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://speech.ee.ntu.edu.tw/~tlkagk/courses.html)

- 《迁移学习导论》助你快速入门迁移学习！ [书的主页](http://jd92.wang/tlbook)

  - 迁移学习统一代码库：[Domain adaptation](https://github.com/jindongwang/transferlearning/tree/master/code/DeepDA) | [Domain generalization](https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG) | [更多代码](https://github.com/jindongwang/transferlearning)

- 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://www.kaggle.com/competitions?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://zhuanlan.zhihu.com/p/25686876) [可以用来练手的数据集](https://www.kaggle.com/annavictoria/ml-friendly-public-datasets/notebook)

其他有用的资料：

- 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://github.com/ctgk/PRML)

- [机器学习算法Python实现](https://github.com/lawlite19/MachineLearning_Python)

- [吴恩达新书：Machine Learning Yearning中文版](https://pan.baidu.com/s/10kosKx6rDguS4tPejY-fRw)

- 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://github.com/exacity/deeplearningbook-chinese)就够了。这本书同时也是**深度学习**经典之书。

- 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://lamda.nju.edu.cn/weixs/book/CNN_book.html)

- - -

[一个简洁明了的时间序列处理(分窗、特征提取、分类)库：Seglearn](https://dmbee.github.io/seglearn/index.html)

[计算机视觉这一年：这是最全的一份CV技术报告](https://zhuanlan.zhihu.com/p/31430602)

[深度学习(花书)中文版](https://github.com/exacity/deeplearningbook-chinese)

**[深度学习最值得看的论文](http://www.dlworld.cn/YeJieDongTai/4385.html)**

**[最全面的深度学习自学资源集锦](http://dataunion.org/29975.html)**

**[Machine learning surveys](https://github.com/metrofun/machine-learning-surveys/)**

**[快速入门TensorFlow](https://github.com/aymericdamien/TensorFlow-Examples)**

[自然语言处理数据集](http://abunchofdata.com/datasets-for-natural-language-processing/)

 

[Learning Machine Learning? Six articles you don’t want to miss](http://www.ibmbigdatahub.com/blog/learning-machine-learning-six-articles-you-don-t-want-miss)

[Getting started with machine learning documented by github](https://github.com/collections/machine-learning)

- - -

## 研究领域资源细分

- ### [深度学习 Deep learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

- ### [强化学习 Reinforcement learning](https://github.com/aikorea/awesome-rl)

- ### [迁移学习 Transfer learning](https://github.com/jindongwang/transferlearning)

- ### [分布式学习系统 Distributed learning system](https://github.com/theanalyst/awesome-distributed-systems)

- ### [计算机视觉/机器视觉 Computer vision / machine vision](https://github.com/jbhuang0604/awesome-computer-vision)

- ### [自然语言处理 Natural language procesing](https://github.com/Nativeatom/NaturalLanguageProcessing)

- ### [生物信息学 Bioinfomatics](https://github.com/danielecook/Awesome-Bioinformatics)

- ### [行为识别 Activity recognition](https://github.com/jindongwang/activityrecognition)

- ### [多智能体 Multi-Agent](http://ddl.escience.cn/f/ILKI)

- - -

##  开始学习：预备知识 Prerequisite

- [学习知识与路线图](https://metacademy.org/)

- [MIT线性代数课堂笔记(中文)](https://github.com/zlotus/notes-linear-algebra)

- [概率与统计 The Probability and Statistics Cookbook](http://statistics.zone/)

- Python

    - [Learn X in Y minutes](https://learnxinyminutes.com/docs/python/)

    - [Python机器学习互动教程](https://www.springboard.com/learning-paths/machine-learning-python/)

- Markdown

    - [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) - Markdown is a easy-to-use writing tool on the GitHu.

- R

    - [R Tutorial](http://www.cyclismo.org/tutorial/R/)

- Python和Matlab的一些cheat sheet：http://ddl.escience.cn/f/IDkq 包含：

    - Numpy、Scipy、Pandas科学计算库

    - Matlab科学计算

    - Matplotlib画图

- 深度学习框架

    - Python

        - [TensorFlow](https://www.tensorflow.org/)

        - [Scikit-learn](http://scikit-learn.org/)

        - [PyTorch](http://pytorch.org/)

        - [Keras](https://keras.io/)

        - [MXNet](http://mxnet.io/)|[相关资源大列表](https://github.com/chinakook/Awesome-MXNet)

        - [Caffe](http://caffe.berkeleyvision.org/)

        - [Caffe2](https://caffe2.ai/)

    - Java

        - [Deeplearning4j](https://deeplearning4j.org/)

    - Matlab

        - [Neural Network Toolbox](https://cn.mathworks.com/help/nnet/index.html)

        - [Deep Learning Toolbox](https://cn.mathworks.com/matlabcentral/fileexchange/38310-deep-learning-toolbox)

- - -

## 文档 notes

- [综述文章汇总](https://github.com/allmachinelearning/MachineLearning/blob/master/notes/survey_readme.md)

- [近200篇机器学习资料汇总！](https://zhuanlan.zhihu.com/p/26136757)

- [机器学习入门资料](https://github.com/allmachinelearning/MachineLearning/blob/master/notes/MLMaterials.md)

- [MIT.Introduction to Machine Learning](http://ddl.escience.cn/f/Iwtu)

- [东京大学同学做的人机交互报告](https://github.com/allmachinelearning/MachineLearning/blob/master/notes/FieldResearchinChina927-104.pdf)

- [人机交互简介](https://github.com/jindongwang/HCI)

- [人机交互与创业论坛](https://github.com/allmachinelearning/MachineLearning/blob/master/notes/%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)

- [职场机器学习入门](https://github.com/allmachinelearning/MachineLearning/blob/master/notes/%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)

- [机器学习的发展历程及启示](http://mt.sohu.com/20170326/n484898474.shtml), (@Prof. Zhihua Zhang/@张志华教授)

- [常用的距离和相似度度量](https://github.com/allmachinelearning/MachineLearning/blob/master/notes/distance%20and%20similarity.md)

- - -

## 课程与讲座 Course and talk

### 机器学习 Machine Learning

 

[台湾大学应用深度学习课程](https://www.csie.ntu.edu.tw/~yvchen/f106-adl/index.html)

- [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://www.datasciencecentral.com/profiles/blogs/neural-networks-for-machine-learning)

  

- [斯坦福机器学习入门课程](https://www.coursera.org/learn/machine-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

- [Neural Networks for Machine Learning](https://www.coursera.org/learn/neural-networks), Coursera上的著名课程，由Geoffrey Hinton教授主讲。

- [Stanford CS 229](http://cs229.stanford.edu/materials.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://github.com/Kivy-CN/Stanford-CS-229-CN)。

- [CMU 10-702 Statistical Machine Learning](http://www.stat.cmu.edu/~larry/=sml/), 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

- [CMU 10-715 Advanced Introduction to Machine Learning](https://www.cs.cmu.edu/~epxing/Class/10715/)，同样是CMU phd级别的课，节奏快难度高

- [机器学习基石](https://www.coursera.org/course/ntumlone)（适合入门）。国立台湾大学[林轩田](https://www.coursera.org/instructor/htlin)

- [机器学习技法](https://www.coursera.org/course/ntumltwo)（适合提高）。国立台湾大学[林轩田](https://www.coursera.org/instructor/htlin)

- [Machine Learning for Data Analysis](https://www.coursera.org/learn/machine-learning-data-analysis), Coursera上Wesleyan大学的Data Analysis and Interpretation专项课程第四课。

- 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://www.youtube.com/playlist?list=PLqJm7Rc5-EXFv6RXaPZzzlzo93Hl0v91E),仔细翻这个频道还可以找到2015的暑期学校视频

- 知乎Live：[我们一起开始机器学习吧](https://www.zhihu.com/lives/792423196996546560)，[机器学习入门之特征工程](https://www.zhihu.com/lives/819543866939174912)

### 深度学习 Machine Learning

- 斯坦福大学Feifei Li教授的[CS231n系列深度学习课程](http://cs231n.stanford.edu/)。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://zhuanlan.zhihu.com/p/21930884)。

- [CS224n: Natural Language Processing](http://cs224n.stanford.edu). Course instructors: Chris Manning, Richard Socher.

### 强化学习 Machine Learning

- [CS 294 Deep Reinforcement Learning, Fall 2017](http://rll.berkeley.edu/deeprlcourse/). Course instructors: Sergey Levine, John Schulman, Chelsea Finn.

- [UCL Course on RL](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)

- [CS234: Reinforcement Learning](http://web.stanford.edu/class/cs234/index.html). 暂无视频

- - -

## 相关书籍 reference book

- [Hands on Machine Learning with Scikit-learn and Tensorflow](https://my.pcloud.com/publink/show?code=XZ9ev77Zk2l6xcMtfIhHm7mRKAYhISb6sl3k)

- 入门读物 [The Elements of Statistical Learning(英文第二版),The Elements of Statistical Learning.pdf](http://ddl.escience.cn/ff/emZH)

- [机器学习](https://book.douban.com/subject/26708119/), (@Prof. Zhihua Zhou/周志华教授)

- [统计学习方法](https://book.douban.com/subject/10590856/), (@Dr. Hang Li/李航博士)

- [一些Kindle读物](http://ddl.escience.cn/f/IwWE):

	- 利用Python进行数据分析

	- 跟老齐学Python：从入门到精通

	- Python与数据挖掘 (大数据技术丛书) - 张良均

	- Python学习手册

	- Python性能分析与优化

	- Python数据挖掘入门与实践

	- Python数据分析与挖掘实战(大数据技术丛书) - 张良均

	- Python科学计算(第2版)

	- Python计算机视觉编程 [美] Jan Erik Solem

	- python核心编程(第三版)

	- Python核心编程（第二版）

	- Python高手之路 - [法] 朱利安·丹乔（Julien Danjou）

	- Python编程快速上手 让繁琐工作自动化

	- Python编程：从入门到实践

	- Python3 CookBook中文版

	- 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

	- 机器学习系统设计 (图灵程序设计丛书) - [美]Willi Richert & Luis Pedro Coelho

	- 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

	- 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）

	- 机器学习：实用案例解析

  

- [数学](https://mega.nz/#F!WVAlGL6B!mqIjYoTjiQnO4jBGVLRIWA

):

    - Algebra - Michael Artin

    - Algebra - Serge Lang

    - Basic Topology - M.A. Armstrong

    - Convex Optimization by Stephen Boyd & Lieven Vandenberghe

    - Functional Analysis by Walter Rudin

    - Functional Analysis, Sobolev Spaces and Partial Differential Equations by Haim Brezis

    - Graph Theory - J.A. Bondy, U.S.R. Murty

    - Graph Theory - Reinhard Diestel

    - Inside Interesting Integrals - Pual J. Nahin

    - Linear Algebra and Its Applications - Gilbert Strang

    - Linear and Nonlinear Functional Analysis with Applications - Philippe G. Ciarlet

    - Mathematical Analysis I - Vladimir A. Zorich

    - Mathematical Analysis II - Vladimir A. Zorich

    - Mathematics for Computer Science - Eric Lehman, F Thomson Leighton, Alber R Meyer

    - Matrix Cookbook, The - Kaare Brandt Petersen, Michael Syskind Pedersen

    - Measures, Integrals and Martingales - René L. Schilling

    - Principles of Mathematical Analysis - Walter Rudin

    - Probabilistic Graphical Models: Principles and Techniques - Daphne Koller, Nir Friedman

    - Probability: Theory and Examples - Rick Durrett

    - Real and Complex Analysis - Walter Rudin

    - Thomas' Calculus - George B. Thomas

    - 普林斯顿微积分读本 - Adrian Banner

- [Packt每日限免电子书精选](http://ddl.escience.cn/f/IS4a):

	- Learning Data Mining with Python

	- Matplotlib for python developers

	- Machine Learing with Spark

	- Mastering R for Quantitative Finance

	- Mastering matplotlib

	- Neural Network Programming with Java

	- Python Machine Learning

	- R Data Visualization Cookbook

	- R Deep Learning Essentials

	- R Graphs Cookbook second edition

	- D3.js By Example 

	- Data Analysis With R

	- Java Deep Learning Essentials

	- Learning Bayesian Models with R

	- Learning Pandas

	- Python Parallel Programming Cookbook

	- Machine Learning with R

---

## 其他 Miscellaneous

- [机器学习日报](http://forum.ai100.com.cn/)：每天更新学术和工业界最新的研究成果

- [机器之心](https://www.jiqizhixin.com/)

- [集智社区](https://jizhi.im/index)

- - -

## 如何加入 How to contribute

如果你对本项目感兴趣，非常欢迎你加入！

- 正常参与：请直接fork、pull都可以

- 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：

	- (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。

	- (墙外)首先在[UPLOAD](https://my.pcloud.com/#page=puplink&code=4e9Z0Vwpmfzvx0y2OqTTTMzkrRUz8q9V)直接上传（**不**需要注册账号）；上传成功后，在[DOWNLOAD](https://my.pcloud.com/publink/show?code=kZWtboZbDDVguCHGV49QkmlLliNPJRMHrFX)里找到你刚上传的文件，共享链接即可。

## 如何开始项目协同合作

[快速了解github协同工作](http://hucaihua.cn/2016/12/02/github_cooperation/)

[及时更新fork项目](https://jinlong.github.io/2015/10/12/syncing-a-fork/)

#### [贡献者 Contributors](https://github.com/allmachinelearning/MachineLearning/blob/master/contributors.md)