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Neural Networks

An Introduction

  • Book
  • © 1995

Overview

Part of the book series: Physics of Neural Networks (NEURAL NETWORKS)

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Table of contents (29 chapters)

  1. Models of Neural Networks

  2. Statistical Physics of Neural Networks

Keywords

About this book

Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.

Reviews

"I have enjoyed using the previous edition of this well-known book both as a personal text and as a class manual. Although it claims to be only an introduction, it contains a wealth of material and addresses real problems in physics." Computing Reviews

Authors and Affiliations

  • Department of Physics, Duke University, Durham, USA

    Berndt Müller, Michael T. Strickland

  • Institut für Theoretische Physik, J.-W.-Goethe-Universität, Frankfurt, Germany

    Joachim Reinhardt

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