An Introduction to Neural Networks - Tapa dura

Gurney, Kevin

9781857286731: An Introduction to Neural Networks

Tapa dura

ISBN 10: 1857286731 ISBN 13: 9781857286731

Editorial: CRC Press, 1997

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Book by Gurney Kevin

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Rese�a del editor

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Rese�a del editor

This key 'user-friendly' feature notwithstanding, the book provides a full level of explanation of the technical aspects of the subject, which non- mathematical rivals usually fail to provide, thereby leaving those areas obscure. Although the study of neural networks is underpinned by ideas that are often best described mathematically, the fundamentals of the subject are accessible without the full mathematical apparatus, as this treatment amply demonstrates. The book provides comprehensive coverage of the following key areas: artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation, which disentangles features specific to separate levels of discussion.Finally, a chapter is devoted to organizing the study of neural networks in various ways, and it attempts to overcome the general impression that it is a loose-knit collection of structures and recipes. The primary aim of the book is to provide an understanding of basic principles, but it also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

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