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  • 资源|5本深度学习和10本机器学习书籍(免费下载)

    作者:lily

    5本深度学习书籍资源推荐

    深度学习(Deep Learning)byIan Goodfellow and Yoshua Bengio and Aaron Courville

    中文版下载地址:https://github.com/exacity/deeplearningbook-chinese

    R语言深度学习实践指南(Deep Learning Made Easy with R)by Dr. N.D. Lewis

    下载地址:http://download.csdn.net/detail/oscer2016/9829915

    深度学习基础(Fundamentals of Deep Learning)by Nikhil Buduma

    下载地址:http://www.taodocs.com/p-32598980.html

    神经网络和统计学习(Neural networks and statistical learning) by K.-L. Du and M.N.s. Swamy

    下载地址:http://download.csdn.net/detail/oscer2016/9829919

    神经网络和深度学习(Neural Networks and Deep Learning) by Michael Niels

    下载地址:http://download.csdn.net/download/newhotter/9651111

    10本机器学习书籍资源推荐

    机器学习、神经网络和统计分类(Machine Learning, Neural Networks, and Statistical Classification)by

    D. Michie, D.J. Spiegelhalter, C.C. Taylor

    下载地址:http://www1.maths.leeds.ac.uk/~charles/statlog/

    贝叶斯推理和机器学习(Bayesian Reasoning and Machine Learning)by David Barber

    下载地址:http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online

    机器学习的高斯过程(Gaussian Processes for Machine Learning) by Carl Edward Rasmussen and Christopher K. I. Williams,The MIT Press

    下载地址:http://www.gaussianprocess.org/gpml/

    信息理论、推理和学习算法(Information Theory, Inference, and Learning Algorithms) by David J.C. MacKay

    下载地址:http://www.inference.phy.cam.ac.uk/mackay/itprnn/book.html

    统计学习元素(The Elements of Statistical Learning)by Trevor Hastie, Robert Tibshirani, Jerome Friedman

    下载地址:http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

    机器学习课程(A Course in Machine Learning)by Hal Daumé III

    下载地址:http://ciml.info/

    机器学习导论(Introduction to Machine Learning)by Amnon Shashua,Cornell University

    下载地址:https://arxiv.org/abs/0904.3664v1

    强化学习(Reinforcement Learning)

    下载地址:https://www.intechopen.com/books/reinforcement_learning

    机器学习导论(Introduction to Machine Learning)- By Nils Nilsson

    下载地址:http://ai.stanford.edu/~nilsson/mlbook.html

    强化学习(Reinforcement Learning)- MIT Press

    下载地址:http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html

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  • 原文地址:https://www.cnblogs.com/Eufisky/p/8974970.html
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