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Introduction To The Theory Of Neural Computation (Santa Fe Institute Series)
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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.
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From the Back Cover
This book is a comprehensive introduction to the neural network models currently under intensive study for computational applications. It is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning. It also provides coverage of neural network applications in a variety of problems of both theoretical and practical interest.
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Product details
Series: Santa Fe Institute Series (Book 1)
Paperback: 350 pages
Publisher: Westview Press; 1 edition (June 26, 1991)
Language: English
ISBN-10: 0201515601
ISBN-13: 978-0201515602
Product Dimensions:
6 x 0.8 x 9 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review:
4.0 out of 5 stars
5 customer reviews
Amazon Best Sellers Rank:
#294,244 in Books (See Top 100 in Books)
To really enjoy this book, one has to co-read a book in Statistical (Quantum) Mechanics, which in itself could be very involved. However, if you follow "Statistical Mechanics: A Survival Guide" by A. M. Glazer it will be an enjoyable read. The idea is to see how theoretical physics has informed the information processing in neural networks.
I think I even saw a cover picture with the name John A Hertz in the cover, not just John Hertz, but I have the vague memory I saw the pun of being Hertz and having a middle H initial AND another h in the name. I purchased my copy in another country just as it was published, but the copy was stolen in NYC (sic). I remember I did have to correct a minus sign typo in one of the equations, but now I a unsure as to the name of the author or the number of editions. I will review it thoroughly hoping there are no LOST SECTIONS. This mathematical treatment is really necessary and the bibliography looks very valuable.
The book needs a improvment in the ideas structure. The contents is ok, but it is really necessary reorganize it
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.
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.
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