Title: Introduction To The Theory Of Neural Computation, Volume I (SANTA FE INSTITUTE STUDIES IN THE SCIENCES OF COMPLEXITY LECTURE NOTES)
Publisher: Addison-Wesley Pub. Co, Redwood City, CA
Publication Date: 1991
Language: English
ISBN 10: 0201503956
ISBN 13: 9780201503951
Binding: hardcover
Condition: New
Weight: 25 ounces

About this title

Synopsis

A clear exposition of the theoretical aspects of neural computation. No specific background other than mathematics (multi-variate calculus, differential equations, and linear algebra) is assumed. Annotation copyright Book News, Inc. Portland, Or.

Review

This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics. Introduction to the Theory of Neural Computation uses these powerful tools to analyze neural networks as associative memory stores and solvers of optimization problems. A detailed analysis of multi-layer networks and recurrent networks follow. The book ends with chapters on unsupervised learning and a formal treatment of the relationship between statistical mechanics and neural networks. Little information is provided about applications and implementations, and the treatment of the material reflects the background of the authors as physicists. However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics. All of the necessary tools are introduced in the book.

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