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Synopsis
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.
"synopsis" may belong to another edition of this title.
Synopsis
Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. The author tackles virtually all aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods; associative memory and Hopfield nets; and, self-organization and feature maps. The book provides a concrete focus through several real-world examples. This feature broadens the book's audience to include both students and professionals in cognitive science, psychology, and computer science as well as those involved in the design, construction, and management of networks.
About the Author
Gurney; K. University of Sheffield, UK,
"About this title" may belong to another edition of this title.
- PublisherCRC Press
- Publication date1997
- ISBN 10 1857285034
- ISBN 13 9781857285031
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages246
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An Introduction to Neural Networks
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An Introduction to Neural Networks
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An Introduction to Neural Networks
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Taylor & Francis Ltd, United Kingdom, London, 1997
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Paperback. Condition: Very Good. 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. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Seller Inventory # GOR001302423
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An Introduction to Neural Networks
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An Introduction to Neural Networks
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