Open Source Book Collection
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Richard S. Sutton and Andrew G. Barto (2018).
Reinforcement Learning: An Introduction.
MIT press, 2nd Edition.
incompleteideas.net/book/the-book.html
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016).
Deep Learning.
MIT Press.
www.deeplearningbook.org
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Jake VanderPlas (2016).
Python Data Science Handbook: Essential Tools for Working with Data.
O'Reilly Media, Inc.
jakevdp.github.io/PythonDataScienceHandbook
    (see errata
here)
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Jake VanderPlas (2015).
A Whirlwind Tour of Python.
O'Reilly Media, Inc.
jakevdp.github.io/WhirlwindTourOfPython
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Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2013).
An Introduction to Statistical Learning with Applications in R.
Springer.
faculty.marshall.usc.edu/gareth-james/ISL
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Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009).
The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Springer, 2nd Edition.
web.stanford.edu/~hastie/ElemStatLearn
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Trevor Hastie, Robert Tibshirani, and Martin Wainwright (2015).
Statistical Learning with Sparsity: The Lasso and Generalizations.
CRC Press.
web.stanford.edu/~hastie/StatLearnSparsity
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LibreTexts, a multi-institutional collaborative venture to develop the next generation of open-access texts
to improve postsecondary education at all levels of higher learning.
Libraries include
Mathematics,
Statistics,
Physics,
Engineering,
Business,
and so on.
libretexts.org
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