An Introduction to Neural Networks [Hardcover]

Kevin Gurney (Author)

Book Description

ISBN-10: 1857286731 | ISBN-13: 978-1857286731 | Publication Date: August 5, 1997

Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.

Editorial Reviews

About the Author

Gurney; K. University of Sheffield, UK, --This text refers to the Paperback edition.

Product Details

• Hardcover: 248 pages
• Publisher: CRC Press (August 5, 1997)
• Language: English
• ISBN-10: 1857286731
• ISBN-13: 978-1857286731
• Product Dimensions: 9.5 x 6.4 x 0.8 inches
• Shipping Weight: 1.2 pounds
• Average Customer Review: 4.5 out of 5 stars  See all reviews (4 customer reviews)
• Amazon Best Sellers Rank: #4,267,663 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

19 of 20 people found the following review helpful:

4.0 out of 5 stars A good introduction without serious math, March 31, 2001

By A Customer

This review is from: An Introduction to Neural Networks (Paperback)

I checked this book out of my university library and was pleasently suprised, although this is by no means a comprehensive book for those who are taking a high level course on AI/neural nets it serves as a very good and complete introduction to those of us who wish to learn more about a very interesting area of computing. After an introduction and review of notation, several basic models are introduced starting with the TLU and progressively presenting more advanced models.

The book is not aimed towards computer science students, and also has in mind other backgrounds. It does however require a sufficient background in science/math (basic algebra and geometry, vectors).

12 of 12 people found the following review helpful:

5.0 out of 5 stars Concise but explanatory, February 17, 2006

By 

Shadman Zafar "Shadman Zafar" (Dallas, TX) - See all my reviews
(REAL NAME)   

Amazon Verified Purchase

This review is from: An Introduction to Neural Networks (Paperback)

This is one of the best written books on NN. This book has that rare quality of being succinct but clearly written so that it can be understood by reasonably mathematical minded individual.

It covers most of the basic topics (back propagation, feed forward, Hopfield nets etc) and gives idiosyncrasies of the field. It also gives you a simple but accurate understanding of the mathematics and algorithms for the field.

I would highly recommend this book to anyone just entering into the field and has some mathematical background.

8 of 8 people found the following review helpful:

4.0 out of 5 stars Just what I needed, September 27, 2007

By 

Marshal Dillon (New Hampshire) - See all my reviews

Amazon Verified Purchase

This review is from: An Introduction to Neural Networks (Paperback)

This book got me started with neural networks. Prior to this book I had only read some articles and didn't quite know what was going on. Now I have an application that makes football predictions straight up. It does not go overboard with math but there are certainly some deep sections. Even if you are using someone else's neural network objects, this is a good read to help you understand the concepts behind NN and what type you want to use.