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.

Product Details

• Amazon Sales Rank: #991335 in Books
• Published on: 1998-08-07
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 842 pages

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—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.
  FEATURES
  • 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.

Customer Reviews

Don't use for self study. Often unclear, contains errors.
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.

Comprehensive introduction to Neural Networks
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!!

Informative and masterfully written.
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+