Neural Networks: An Introduction
| AUTHOR | Mller, Berndt; Strickland, Michael T.; Reinhardt, Joachim et al. |
| PUBLISHER | Springer (10/02/1995) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
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.
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Product Format
Product Details
ISBN-13:
9783540602071
ISBN-10:
3540602070
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
Edition Number:
0002
More Product Details
Page Count:
331
Carton Quantity:
24
Product Dimensions:
6.08 x 0.84 x 9.20 inches
Weight:
1.29 pound(s)
Feature Codes:
Illustrated
Country of Origin:
DE
Subject Information
BISAC Categories
Computers | Data Science - Neural Networks
Computers | Artificial Intelligence - General
Dewey Decimal:
006.3
Library of Congress Control Number:
95024948
Descriptions, Reviews, Etc.
annotation
The second edition of this widely-acclaimed book presents a concise overview of the concepts of neural-network models and the techniques of parallel distributed processing. The authors have broken down neural networks to a comprehensive three-part treatment with a multi-disciplinary approach.
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jacket back
Neural Networks The concepts of neural-network models and techniques of parallel distributed processing are comprehensively presented in a three-step approach: - After a brief overview of the neural structure of the brain and the history of neural-network modeling, the reader is introduced to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers more advanced subjects such as the 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. - In the self-contained final part, seven programs that provide practical demonstrations of neural-network models and their learning strategies are discussed. The software is included on a 3 1/2-inch MS-DOS diskette. The source code can be modified using Borland's TURBO-C 2.0 compiler, the Microsoft C compiler (5.0), or compatible compilers.
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publisher marketing
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.
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