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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning Paperback – 1 Apr 2019


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Product details

  • Paperback: 216 pages
  • Publisher: Sebtel Press (1 April 2019)
  • Language: English
  • ISBN-10: 0956372813
  • ISBN-13: 978-0956372819
  • Product Dimensions: 15.2 x 1.2 x 22.9 cm
  • Boxed-product Weight: 340 g
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: 18,183 in Books (See Top 100 in Books)

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Review

"Artificial Intelligence Engines will introduce you to the rapidly growing field of deep learning networks: how to build them, how to use them; and how to think about them. James Stone will guide you from the basics to the outer reaches of a technology that is changing the world."

Professor Terrence Sejnowski, Director of the Computational Neurobiology Laboratory, Salk Institute, USA. Author of The Deep Learning Revolution, MIT Press, 2018.

"This book manages the impossible: it is a fun read, intuitive and engaging, lighthearted and delightful, and cuts right through the hype and turgid terminology. Unlike many texts, this is not a shallow cookbook for some particular deep learning program-du-jure. Instead, it crisply and painlessly imparts the principles, intuitions and background needed to understand existing machine-learning systems, learn new tools, and invent novel architectures, with ease."

Professor Barak Pearlmutter, Brain and Computation Laboratory, National University of Ireland Maynooth, Ireland.

"This text provides an engaging introduction to the mathematics underlying neural networks. It is meant to be read from start to finish, as it carefully builds up, chapter by chapter, the essentials of neural network theory. After first describing classic linear networks and nonlinear multilayer perceptrons, Stone gradually introduces a comprehensive range of cutting edge technologies in use today. Written in an accessible and insightful manner, this book is a pleasure to read, and I will certainly be recommending it to my students."

Dr Stephen Eglen, Department of Applied Mathematics and Theoretical Physics (DAMTP), Cambridge Computational Biology Institute (CCBI), Cambridge University, UK.


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