Simulating Neural Networks with Mathematica [Paperback]

James A. Freeman (Author)

Book Description

Publication Date: April 10, 1993 | ISBN-10: 020156629X | ISBN-13: 978-0201566291 | Edition: 1st

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

Editorial Reviews

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

Product Details

• Paperback: 352 pages
• Publisher: Addison-Wesley Professional; 1st edition (April 10, 1993)
• Language: English
• ISBN-10: 020156629X
• ISBN-13: 978-0201566291
• Product Dimensions: 9.5 x 6.6 x 0.8 inches
• Shipping Weight: 1 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #563,246 in Books (See Top 100 in Books)
  • #77 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Neural Networks

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Customer Reviews

Most Helpful Customer Reviews

12 of 12 people found the following review helpful:

5.0 out of 5 stars A clear way to see how Neural Networks work., November 23, 1995

By A Customer

This review is from: Simulating Neural Networks with Mathematica (Paperback)

This is another book where the capabilities of Mathematica
are put to good use. Clear explanations and code make it
a joy to go through and do all the calculational stuff.
Helps even quite experienced people to visualise some of the concepts they
may not be experienced with.
All the basic models are dealt with. The last chapter on genetic algorithms
is a bonus.

9 of 10 people found the following review helpful:

4.0 out of 5 stars Quite satisfied with the book., September 26, 2000

By 

Von Whitley "Physicist and egg sandwich expert" (Washington, DC) - See all my reviews
(REAL NAME)   

This review is from: Simulating Neural Networks with Mathematica (Paperback)

Overall, I am quite happy with the book. It does exactly as it describes...shows the reader how to use mathematica to simulate several types of Neural Networks. The code is clear, fairly short and the example networks fun to work though. The flexibilty of Mathematica made it a simple task to view what the networks were doing and thus made the networks easier to understand.

My only complaint is that the book is too short. Part of this complaint is that I really enjoyed playing with the example nets and hated to see it end. However, only about 8 networks are mentioned and each is covered in 20-30 pages...program code included. I wish the author had time to double or triple the size of the book to cover the nets more thoroughly and to cover others.

The book is also a bit shy on the mathematical treatment of the networks. It does have some math, but the derivations included are not that rigorous. I have supplemented this book with others to cover the theory.

Nevertheless, If you're wanting to use Mathematica for Neural Networking and you're having difficulty getting started, this book is worth the price.

5.0 out of 5 stars No experience needed, all apply, February 10, 2012

By 

Marc Mest - See all my reviews
(REAL NAME)   

Amazon Verified Purchase

This review is from: Simulating Neural Networks with Mathematica (Paperback)

This is a great starter for neural networks. It is both a tutorial on neural networks and you write the code as you go.

I do not think it matters which version of Mathematica you are using. No previous knowledge is required.

This book is structured so that the first few chapters introduce the concepts, and the are various applications. Even the classic Traveling salesperson. The last few chapters are some very specific applied theory. Adaptive Resonance Theory was very interesting.

Overall this book is both a fun and educational book.