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Editorial Reviews
Amazon.com
This book provides a solid statistical foundation for neural networks from a pattern recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of math knowledge necessary for an undergraduate science degree.
All Customer Reviews
Avg. Customer Review:
Number of Reviews: 5
Write an online review and share your thoughts with other readers!
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1 of 2 people found the following review helpful:
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Excellent mathematical reference of Neural Networks
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April 6, 2000
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Reviewer:
Ariel Joel Sandez
from Argentina
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A good book if you are looking for learning mathematical teory of Neural Networks or set a parameters of comercial application. Not recommended for beginners
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10 of 10 people found the following review helpful:
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Extraordinarily well written and comprehensive
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July 8, 1999
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Reviewer:
plgabel@arity.com
from Concord, Massachusetts
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Rarely do I encounter a book of such technical quality that also is a pleasure to read. Bishop moves through sometimes difficult topics in a clear, well-motivated style that is appropriate as both an introduction and a desktop reference on neural nets. Definitely on the "A list." Bishop chose to not include discussions on a number of topics that might have diluted his focus on pattern recognition (for example, Hebbian learning and neural net approaches to principal components analysis). I think that these choices greatly strengthened the integrity of his presentation. I would love to see an updated edition with a discussion of recent results in statistical learning theory, kernel methods and support vector machines.
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7 of 7 people found the following review helpful:
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Excellent technical reference and tutorial
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June 20, 1999
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Reviewer:
A customer
from UK
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I'd like to agree with previous reviewers. Note that you will need a good mathematical background (especially in statistics) to understand the content. However, the book is completely thorough in developing all the key concepts and really tries to give you insight into the meaning behind the equations. It's style is that of an undergraduate level textbook, but a very well written one. To use neural nets effectively, I think you need to have at least one book like this.
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6 of 6 people found the following review helpful:
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A Thorough and Rigorous Introduction
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November 7, 1997
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Reviewer:
A customer
from San Antonio, Texas
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This is a terrific book if you want to understand why neural nets work, and how to make them work. As advertised, it really goes into practical issues like preprocessing and generalization, which are easy to do halfheartedly, but are complex issues if you really want to get the best results. If I had to have only one book on neural nets, this would be it, no contest.
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