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Message from Dean - May 8th 2007
I am currently testing out a new version of the APF Bridge Component - If you notice any errors within this demo store please drop me a line.
List Price: $59.00Amazon.com's Price: $49.33 You Save: $9.67 (16%)Prices subject to change.
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Binding: Paperback
Dewey Decimal Number: 006.3
EAN: 9780201515602
ISBN: 0201515601
Label: Westview Press
Manufacturer: Westview Press
Number Of Items: 1
Number Of Pages: 352
Publication Date: January 01, 1991
Publisher: Westview Press
Studio: Westview Press
Alternate Versions: Click to Display
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Editorial Review:
Amazon.com Review:
This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics. Introduction to the Theory of Neural Computation uses these powerful tools to analyze neural networks as associative memory stores and solvers of optimization problems. A detailed analysis of multi-layer networks and recurrent networks follow. The book ends with chapters on unsupervised learning and a formal treatment of the relationship between statistical mechanics and neural networks. Little information is provided about applications and implementations, and the treatment of the material reflects the background of the authors as physicists. However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics. All of the necessary tools are introduced in the book.
Product Description:
Comprehensive introduction to the neural network models currently under intensive study for computational applications. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest. DLC: 1. Neural computers.
Average Rating: 
Rating:  -
It's not the latest book on this topic, so today, there are other texts that have more recent developments to be sure. I originally read this text about 15 years ago. But what I got from this book, that I didn't get from most, are important insights and clear understanding of the material that's covered. The authors have a deep understanding, and have teaching as their goal in writing. Most other texts in this area are lacking in one or both of those characteristics, and aren't worth the paper they are printed on.
Rating: -
This book is written from a mathematical perspective. The book introduces the Hopfield Neural Network with history and applications. The authors solve the network problem and develop the Hebb Rule. Links are made to Ising Spin models and stochastic problems. I find this book to be one of the best written mathematical guides for Neural Networks.
Rating:  -
This was a good survey, and well-grounded mathematically. It is kind of scattershot, and if you primarily want to do practical projects like predicting financial markets, a lot of the sections won't be relevant. But if you want a broad-based approach, emphasizing a variety of network designs fro different purposes, this book is very good.
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