Artificial Intelligence Engines by James V Stone

Artificial Intelligence Engines by James V Stone

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.

Stone, James V.: - James V. Stone is a Reader in the Psychology Department of the University of Sheffield. He is coauthor (with John P. Frisby) of the widely used text Seeing: The Computational Approach to Biological Vision (second edition, MIT Press, 2010), and author of Independent Component Analysis: A Tutorial Introduction (MIT Press, 2004).

SKU	Non disponible
ISBN 13	9780956372819
ISBN 10	0956372813
Titre	Artificial Intelligence Engines
Auteur	James V Stone
État	Non disponible
Type de reliure	Paperback
Éditeur	Sebtel Press
Année de publication	2019-04-01
Nombre de pages	218
Note de couverture	La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier.
Note	Non disponible