书籍
- 《Foundations of Machine Learning》 机器学习基础第二版 PDF
- 《Mathematical Analysis of Machine Learning Algorithms》 Tong Zhang PDF
- 《Foundations of Data Science》 数据科学基础 PDF
- 《强化学习中文教程（蘑菇书）》https://github.com/datawhalechina/easy-rl
- Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- 动手学深度学习 https://zh.d2l.ai/
- 动手学强化学习
- 机器翻译：基础与模型
- Convex Optimization
- 《Numerical Optimization》 Jorge NocedalStephen J. Wright
- 《机器翻译：统计建模与深度学习方法》肖桐 朱靖波 著 https://opensource.niutrans.com/mtbook/
- 《南瓜书》 https://datawhalechina.github.io/pumpkin-book
- 《机器学习》（西瓜书）公式推导解析 https://github.com/datawhalechina/pumpkin-book
- 《神经网络与深度学习》 Neural Network and Deep Learning 邱锡鹏
- 《统计学习方法》李航，R 语言实现 https://bookdown.org/lyuchengrui/statisticallearningmethods/
- 《Reinforcement Learning: An Introduction》（第二版）中文翻译网页版
- 《迁移学习简明手册》 https://github.com/jindongwang/transferlearning-tutorial
- 《Tensorflow 内核和实现机制》 https://github.com/horance-liu/tensorflow-internals
- 《神经网络与深度学习》 Neural Networks and Deep Learning Michael Nielsen
- 《Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares》
- 《Data Scientist Handbook》https://bookdown.org/BaktiSiregar/data-science-for-beginners/
- 《Bayesian Data Analysis, 3rd》贝叶斯数据分析第三版 Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari and Donald Rubin https://github.com/avehtari/BDA_course_Aalto PDF
- 《Bayesian Reasoning and Machine Learning》贝叶斯推理与机器学习 David Barber 主页
- 《Foundations of Machine Learning, 2nd》机器学习基石 Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar https://cs.nyu.edu/~mohri/mlbook/ PDF
- 《Pattern Recognition and Machine Learning》模式识别与机器学习 Christopher Bishop https://www.microsoft.com/en-us/research/people/cmbishop/ PDF
- 《Machine Learning: A Bayesian and Optimization Perspective》PDF
- 《Probabilistic Machine Learning: An Introduction》 https://github.com/probml/pml-book 提供 PDF 电子版下载
- 《线性规划》https://github.com/Operations-Research-Science/Ebook-Linear_Programming
- 《Machine Learning Systems: Design and Implementation》机器学习系统：设计和实现 https://github.com/openmlsys/openmlsys-zh
- 《Mathematics for Machine Learning》Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. https://mml-book.github.io/ PDF
- 《Machine Learning: a Probabilistic Perspective》 机器学习：概率视角 补充材料 PDF
- 书籍主页 https://www.cs.ubc.ca/~murphyk/MLbook/
- 软件工具 Matlab/Octave
- 《Gaussian Processes for Machine Learning》 书籍主页
- 《Information Theory, Inference, and Learning Algorithms》书籍主页 软件工具 Octave/J
- 《Hands-on Machine Learning with R》 Bradley Boehmke & Brandon Greenwell. https://bradleyboehmke.github.io/HOML/
- 《特征工程与特征选择》 Feature Engineering and Selection: A Practical Approach for Predictive Models. Max Kuhn and Kjell Johnson. http://www.feat.engineering
- 《可解释的机器学习》 Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Christoph Molnar. https://christophm.github.io/interpretable-ml-book/
- 《机器学习与R语言》Machine Learning in R. https://mlr.mlr-org.com
- 《分类与回归：caret 包》 Classification And REgression Training. Max Kuhn. https://topepo.github.io/caret/
- 《Mastering Spark with R》 Javier Luraschi, Kevin Kuo, Edgar Ruiz. https://therinspark.com
- 《Understanding Deep Learning》Simon J.D. Prince https://udlbook.github.io/udlbook/
- 《Algorithms for Convex Optimization》https://convex-optimization.github.io/
- TensorFlow 2 深度学习开源书(龙书) https://github.com/dragen1860/Deep-Learning-with-TensorFlow-book
- 机器学习算法 Python 实现 https://github.com/lawlite19/MachineLearning_Python
- Python for 《Deep Learning》 https://github.com/MingchaoZhu/DeepLearning 《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现
- 机器学习原理 https://shunliz.gitbooks.io/machine-learning/content/
课程
- 徐亦达 机器学习研究课程 https://github.com/roboticcam/machine-learning-notes
- Bolei Zhou 强化学习纲要 https://github.com/zhoubolei/introRL
博客
- https://www.offconvex.org/
- The Unofficial Google Data Science Blog https://www.unofficialgoogledatascience.com/
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