9780956372819-0956372813-Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

ISBN-13: 9780956372819
ISBN-10: 0956372813
Author: Stone, James V
Publication date: 2019
Publisher: Sebtel Press
Format: Paperback 216 pages
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Book details

ISBN-13: 9780956372819
ISBN-10: 0956372813
Author: Stone, James V
Publication date: 2019
Publisher: Sebtel Press
Format: Paperback 216 pages

Summary

Acknowledged authors Stone, James V wrote Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning comprising 216 pages back in 2019. Textbook and eTextbook are published under ISBN 0956372813 and 9780956372819. Since then Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning textbook was available to sell back to BooksRun online for the top buyback price of $ 3.93 or rent at the marketplace.

Description

"authoritative, funny, and concise"Steven Strogatz, Professor of Applied Mathematics, Cornell University.The brain has always had a fundamental advantage over conventional computers: it can learn. However, a new generation of artificial intelligence algorithms, in the form of deep neural networks, is rapidly eliminating that advantage. Deep neural networks rely on adaptive algorithms to master a wide variety of tasks, including cancer diagnosis, object recognition, speech recognition, robotic control, chess, poker, backgammon and Go, at super-human levels of performance. In this richly illustrated book, key neural network learning algorithms are explained informally first, followed by detailed mathematical analyses. Topics include both historically important neural networks (perceptrons, Hopfield nets, Boltzmann machines and backpropagation networks), and modern deep neural networks (variational autoencoders, convolutional networks, generative adversarial networks, and reinforcement learning using SARSA and Q-learning). Online computer programs, collated from open source repositories, give hands-on experience of neural networks, and PowerPoint slides provide support for teaching. Written in an informal style, with a comprehensive glossary, tutorial appendices (e.g. Bayes' theorem, maximum likelihood estimation), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.Dr James V Stone is an Honorary Reader in Vision and Computational Neuroscience at the University of Sheffield, England.

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