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Deep Learning provides a truly comprehensive look at the state of the art in deep learning and some developing areas of research. The authors are Ian Goodfellow, along with his Ph.D. advisor Yoshua Bengio, and Aaron Courville. All three are widely published experts in the field of artificial intelligence (AI). In addition to being available in both hard cover and Kindle the authors also make the individual chapter PDFs available for free on the Internet. The book is aimed at an academic research audience with prior knowledge of calculus, linear algebra, probability, and some programming capabilities. A non-mathematical reader will find this book difficult. A comprehensive, well cited coverage of the field makes this book a valuable reference for any researcher. The book provides a mathematical description of a comprehensive set of deep learning algorithms, but could benefit from more pseudocode examples. The authors provide an adequate explanation for the many mathematical formulas that are used to communicate the ideas expressed in this book. The lack of both exercises and examples in any of the major machine learning software packages makes this book difficult as a primary undergraduate textbook. While a review of a book focused entirely on deep learning might not be the usual topic for Genetic Programming and Evolvable Machines, there are many areas of interest for the genetic programming (GP) and evolutionary algorithm research communities. The effect of deep learning upon the field of AI has been profound. Deep learning’s application to diverse cases ranging from self-driving cars to the game of Go have been widely reported. This book provides a solid deep learning

Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning