Understanding Neural Networks, Volume 2 (Macintosh version): Advanced Networks

Overview

Understanding Neural Networks is a textbook and workbook that provides a unique interactive learning environment. With or without the aid of a classroom instructor, it allows students and other users to learn about neural networks while gaining practical, hands-on experience with all of the leading network models. Each model is presented as realistically as possible. Also included are chapter exercises and questions, many with illustrations.

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Overview

Understanding Neural Networks is a textbook and workbook that provides a unique interactive learning environment. With or without the aid of a classroom instructor, it allows students and other users to learn about neural networks while gaining practical, hands-on experience with all of the leading network models. Each model is presented as realistically as possible. Also included are chapter exercises and questions, many with illustrations.

The key feature of this workbook is the software. Available for PC-ATs and compatibles and the Macintosh, these disks contain a collection of full-featured commercial-quality simulators for the most important network paradigms. The user interface is graphic and easy to use, and the simulators are consistent across all networks. The simulators can also build and train significantly large networks, allowing users to construct networks on their own with data relevant to their problems.

Volume 1 covers learning, attractor networks, and hierarchical networks (including back-propagation networks). Volume 2 takes up temporal networks (including recurrent networks), self-organizing networks, higher-order networks, and such new directions in neural networks as fuzzy networks and evolutionary networks. Both volumes contain instructions on how to use the workbook, an introduction, appendixes, a table of random numbers, a glossary, a bibliography, and index.

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Product Details

  • ISBN-13: 9780262531030
  • Publisher: MIT Press
  • Publication date: 10/23/1992
  • Series: Bradford Books Series
  • Edition description: SPIRAL
  • Pages: 379
  • Product dimensions: 8.61 (w) x 9.09 (h) x 1.00 (d)

Meet the Author

Maureen Caudill is President of Adaptics, a neural network consulting company in San Diego, and author of the popular "Neural Network Primer" articles that appear regularly in AI Expert.

Charles Butler is a Senior Principal Scientist at Physical Sciences in Alexandria, Virginia. He is a specialist in neural network application development.

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Table of Contents

Acknowledgments
How to Use This Workbook
I Introduction
1 Introduction to the Simulators
2 Knowledge Representation in Neural Networks
II Temporal Networks
3 Sequential Attractor Networks
4 Avalanche Networks
5 Recurrent Networks
III SelfOrganizing Networks
6 Advanced Kohonen Networks
7 Adaptive Resonance Networks
IV HigherOrder Networks
8 The Padaline
9 The Probabilistic Neural Network
V New Directions in Neural Networks
10 Fuzzy Neural Networks
11 Evolutionary Systems
AppendiXes
A User's Guide to PC Simulators
B User's Guide to Macintosh Simulators
C File Formats for the Simulators
Table of Random Numbers
Glossary
Bibliography
Answers to Selected Questions
IndeX
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