![]() Specific areas of coverage are machine learning basics, linear algebra, and numerical computation. The review contained in these first five chapters covers areas of mathematics and computer science that are valuable to any machine learning researcher, including GP and evolutionary algorithms. Chapters 1–5 only present a mathematical overview, the reader is expected to have previously studied each of these topics. The first part, which spans the first five chapters, provides an overview of the prerequisite mathematical concepts that the rest of the book is built upon. The foundation of deep learning implementation are software packages, such as TensorFlow, MXNet and Theano that implement highly efficient computation engines that can be executed over distributed grids of GPUs and CPUs.ĭeep Learning comprises 20 chapters which are divided into three distinct parts: prerequisite knowledge, current mainstream deep learning, and emerging future areas of deep learning research. Evolutionary algorithms are often used to evolve the complex structure of neural networks, an example of this is Kenneth Stanley’s Neuroevolution of Augmenting Topologies (NEAT). ![]() Neural networks are the primary algorithm of deep learning, Neural networks and evolutionary algorithms have seen a great deal of combined research. This book provides a solid deep learning foundation for any AI researcher. Deep learning’s application to diverse cases ranging from self-driving cars to the game of Go have been widely reported. The effect of deep learning upon the field of AI has been profound. 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 lack of both exercises and examples in any of the major machine learning software packages makes this book difficult as a primary undergraduate textbook. The authors provide an adequate explanation for the many mathematical formulas that are used to communicate the ideas expressed in this book. The book provides a mathematical description of a comprehensive set of deep learning algorithms, but could benefit from more pseudocode examples. A comprehensive, well cited coverage of the field makes this book a valuable reference for any researcher. A non-mathematical reader will find this book difficult. Footnote 1 The book is aimed at an academic research audience with prior knowledge of calculus, linear algebra, probability, and some programming capabilities. 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. All three are widely published experts in the field of artificial intelligence (AI). advisor Yoshua Bengio, and Aaron Courville. The authors are Ian Goodfellow, along with his Ph.D. ![]() Deep Learning provides a truly comprehensive look at the state of the art in deep learning and some developing areas of research.
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