Simulating Neural Networks with Mathematica

James Freeman

ISBN 10: 020156629X / ISBN 13: 9780201566291

Published by ADDISON WESLEY PUB CO INC, 2020

About this Item

Zustand: Gut | Seiten: 352 | Sprache: Englisch | Produktart: Sonstiges.

Seller Inventory # 1152476/3

Bibliographic Details

• Title: Simulating Neural Networks with Mathematica
• Publisher: ADDISON WESLEY PUB CO INC
• Publication Date: 2020
• Language: English
• ISBN 10: 020156629X
• ISBN 13: 9780201566291
• Binding: Soft cover
• Condition: Gut
• Seller catalogs:
  • Sonstiges

About this title

Synopsis

Introduces neural networks, their operation and their application, in the context of Mathematica, a mathematical programming language. DLC: Neural networks (Computer science)

From the Back Cover

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