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Simulating Neural Networks with Mathematica

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Simulating Neural Networks with Mathematica

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Features

Features

  • Teaches the reader about what neural networks are, and how to manipulate them within the Mathematica environment.
  • Shows how Mathematica can be used to implement and experiment with neural network architectures.
  • Addresses a major topic related to neural networks in each chapter, or a specific type of neural network architecture.
  • Contains exercises, suggested projects, and supplementary reading lists with each chapter.
  • Includes Mathematica application programs ("packages") in Appendix. (Also available electronically from MathSource.)

Description

  • Copyright 1994
  • Dimensions: 6-1/4x9-1/4
  • Pages: 352
  • Edition: 1st
  • Book
  • ISBN-10: 0-201-56629-X
  • ISBN-13: 978-0-201-56629-1

This book introduces neural networks, their operation, and application, in the context of the interactive Mathematica environment. Readers will learn how to simulate neural network operations using Mathematica, and will learn techniques for employing Mathematica to assess neural network behavior and performance. For students of neural networks in upper-level undergraduate or beginning graduate courses in computer science, engineering, and related areas. Also for researchers and practitioners interested in using Mathematica as a research tool.

Features
  • Teaches the reader about what neural networks are, and how to manipulate them within the Mathematica environment.
  • Shows how Mathematica can be used to implement and experiment with neural network architectures.
  • Addresses a major topic related to neural networks in each chapter, or a specific type of neural network architecture.
  • Contains exercises, suggested projects, and supplementary reading lists with each chapter.
  • Includes Mathematica application programs ("packages") in Appendix. (Also available electronically from MathSource.)
Table of ContentsIntroduction to Neural Networks and Mathematica
Training by Error Minimization
Backpropagation and Its Variants
Probability and Neural Networks
Optimization and Constraint Satisfaction with Neural Networks
Feedback and Recurrent Networks
Adaptive Resonance Theory
Genetic Algorithms


020156629XB04062001

Sample Content

Table of Contents



Introduction to Neural Networks and Mathematica.


Training by Error Minimization.


Backpropagation and Its Variants.


Probability and Neural Networks.


Optimization and Constraint Satisfaction with Neural Networks.


Feedback and Recurrent Networks.


Adaptive Resonance Theory.


Genetic Algorithms. 020156629XT04062001

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