- Hardcover: 842 pages
- Publisher: Prentice Hall; 2 edition (6 July 1998)
- Language English
- ISBN-10: 0132733501
- ISBN-13: 978-0132733502
- Product Dimensions: 23.6 x 18.2 x 3.8 cm
- Average Customer Review: 4.2 out of 5 stars See all reviews (4 customer reviews)
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Amazon Bestsellers Rank:
1,039,785 in Books (See Top 100 in Books)
- #51 in Books > Computers & Internet > Computer Science > Algorithms > Fuzzy Logic
- #59 in Books > Computers & Internet > Computer Science > Artificial Intelligence > Neural Networks
- See Complete Table of Contents
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Product Description
Product Description
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
From the Back Cover
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
NEW TO THIS EDITION- NEW—New chapters now cover such areas as:
- Support vector machines.
- Reinforcement learning/neurodynamic programming.
- Dynamically driven recurrent networks.
- NEW-End—of-chapter problems revised, improved and expanded in number.
- Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications.
- Detailed analysis of back-propagation learning and multi-layer perceptrons.
- Explores the intricacies of the learning process—an essential component for understanding neural networks.
- Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics.
- Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice.
- Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary.
- Includes a detailed and extensive bibliography for easy reference.
- Computer-oriented experiments distributed throughout the book
- Uses Matlab SE version 5.
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Customer Reviews
Most Helpful Customer Reviews
12 of 13 people found the following review helpful
Format:Paperback
This book is fairly mathematically demanding, and when you are tackling such a book you have to rely on the text being accurate. Quite early on, in equation 2.62, there is an error that wasted me two days as I tried to figure out exactly what it was I had missed. I ended up having to refer to the article the material was based on, Geman et al.
From that point on, the clarity of the explanation degraded to the point that I was taking so long understanding it (and with my faith in the accuracy of the information now severely impaired), that I gave up and bought "Introduction to the Theory of Neural Computation", recommended in the comp.ai.neural-nets faq, instead.
The material was not so bad up to that point, and perhaps if you have a tutor to help you when you get stuck it is worth using, but for self-study I would not recommend it. If like me you really want to fully understand the material you will end up wasting a lot of time. If not, there are clearer, simpler books available.
From that point on, the clarity of the explanation degraded to the point that I was taking so long understanding it (and with my faith in the accuracy of the information now severely impaired), that I gave up and bought "Introduction to the Theory of Neural Computation", recommended in the comp.ai.neural-nets faq, instead.
The material was not so bad up to that point, and perhaps if you have a tutor to help you when you get stuck it is worth using, but for self-study I would not recommend it. If like me you really want to fully understand the material you will end up wasting a lot of time. If not, there are clearer, simpler books available.
6 of 8 people found the following review helpful
Format:Paperback
This is a mammoth of a book. Each chapter introduces and developes a particular model of neural network. Really useful for related undergraduate university courses. It's taught me all I know!!
3 of 5 people found the following review helpful
Format:Hardcover
A wonderfully well written, insightful, treatment of artificial neural networks. Beginning from the basics, the author sets forth both a technological and historical perspective for the understanding this multidisiplinary subject area. The book is written from a practical engineering perspective and comprehensively spans the entire discipline of modern neural network theory. A+
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