整理 | 阿司匹林
出品 | 人工智能头条(公众号ID:AI_Thinker)
炎炎夏日,有什么比学习更能振奋人心!
KDnuggets 网站编辑 Matthew Mayo 特意为广大读者挑选了 20 本机器学习和数据科学相关的书籍。
这份书单除了 Ian Goodfellow 等人的 Deep Learning、吴恩达的 Machine Learning Yearning 等经典著作之外,还有 Python、统计学习、贝叶斯理论等相关书籍。
重点是,这些书籍全都可以免费下载或者在线阅读。
一分钱都不用花,妈妈再也不用担心我的学习了~
▌1. Think Stats: Probability and Statistics for Programmers
作者:
Allen B. Downey
地址:
http://www.greenteapress.com/thinkstats/
▌2. Probabilistic Programming & Bayesian Methods for Hackers
作者:
Cam Davidson-Pilon
地址:
http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/#contents
▌3. Understanding Machine Learning: From Theory to Algorithms
作者:
Shai Shalev-Shwartz and Shai Ben-David
地址:
http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/
▌4. The Elements of Statistical Learning
作者:
Trevor Hastie, Robert Tibshirani and Jerome Friedman
地址:
https://web.stanford.edu/~hastie/Papers/ESLII.pdf
▌5. An Introduction to Statistical Learning with Applications in R
作者:
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
地址:
http://www-bcf.usc.edu/~gareth/ISL/
▌6. Foundations of Data Science
作者:
Avrim Blum, John Hopcroft, and Ravindran Kannan
地址:
https://www.cs.cornell.edu/jeh/book.pdf
▌7. A Programmer's Guide to Data Mining: The Ancient Art of the Numerati
作者:
Ron Zacharski
地址:
http://guidetodatamining.com/
▌8. Mining of Massive Datasets
作者:
Jure Leskovec, Anand Rajaraman and Jeff Ullman
地址:
http://mmds.org/
▌9. Deep Learning
作者:
Ian Goodfellow, Yoshua Bengio and Aaron Courville
地址:
http://www.deeplearningbook.org/
▌10. Machine Learning Yearning
作者:
Andrew Ng
地址:
http://www.mlyearning.org/
▌11. Python Data Science Handbook
作者:
Jake VanderPlas
地址:
https://github.com/jakevdp/PythonDataScienceHandbook
▌12. Neural Networks and Deep Learning
作者:
Michael Nielsen
地址:
http://neuralnetworksanddeeplearning.com/
▌13. Think Bayes
作者:
Allen B. Downey
地址:
http://greenteapress.com/wp/think-bayes/
▌14. Machine Learning & Big Data
作者:
Kareem Alkaseer
地址:
http://www.kareemalkaseer.com/books/ml
▌15. Statistical Learning with Sparsity: The Lasso and Generalizations
作者:
Trevor Hastie, Robert Tibshirani, Martin Wainwright
地址:
https://web.stanford.edu/~hastie/StatLearnSparsity/
▌16. Statistical inference for data science
作者:
Brian Caffo
地址:
https://leanpub.com/LittleInferenceBook/read
▌17. Convex Optimization
作者:
Stephen Boyd and Lieven Vandenberghe
地址:
http://stanford.edu/~boyd/cvxbook/
▌18. Natural Language Processing with Python
作者:
Steven Bird, Ewan Klein, and Edward Loper
地址:
https://www.nltk.org/book/
▌19. Automate the Boring Stuff with Python
作者:
Al Sweigart
地址:
https://automatetheboringstuff.com/
▌20. Social Media Mining: An Introduction
作者:
Reza Zafarani, Mohammad Ali Abbasi and Huan Liu
地址:
http://dmml.asu.edu/smm/
参考链接:
https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html
https://www.kdnuggets.com/2018/05/10-more-free-must-read-books-for-machine-learning-and-data-science.html
今晚开播
◆
AI公开课
◆
时间:5月31日 20:00-21:00
点击 | 阅读原文 | 报名参加公开课
扫描海报二维码,添加小助手微信
备注:公开课,加入课程交流群
点击 | 阅读原文 | 报名参加公开课