Title: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
Publisher: Sebtel Press
Publication Date: 2019
Language: English
ISBN 10: 0956372821
ISBN 13: 9780956372826
Binding: Hardcover
Condition: new

About this title

Synopsis

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 (e.g. perceptrons), and modern deep neural networks (e.g. generative adversarial networks). 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), and a list of further readings, this is an ideal introduction to the algorithmic engines of modern artificial intelligence.

About the Author

James V Stone has worked in Geoffrey Hinton's laboratory at the University of Toronto, and in Terry Sejnowski's laboratory the Salk Institute, San Diego.

He is an Honorary Associate Professor in Vision and Computational Neuroscience at the University of Sheffield, England.

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