Neural Networks for Pattern Recognition

by Christopher M. Bishop

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.

Genres: Computer Science, Science, Artificial Intelligence, Mathematics, Reference, Textbooks, Nonfiction

482 pages, Paperback

First published January 18, 1996

Reviews

[Ira Burton · Rating 4 out of 5]

A great book on the use of neural network as they apply to pattern recognition. This book is understandably heavy on the mathematical aspects of things, but the author does so with a grain of salt. This is definitely not a mathematics book.

[Ole Laursen · Rating 5 out of 5]

Most books about neural networks are silent when it comes to less obvious details of what training feedforward networks actually does, and what the result of the network represents. This book analyzes all these details by means of statistics, and consequently gives a very solid foundation for understanding neural networks. The definitive book for feedforward neural networks - extremely good! The math-heavy treatise makes it unsuitable for a beginners book, though.

[Shyam Poovaiah · Rating 5 out of 5]

Along with the regular content has introduction to linear algebra and other mathematical prerquisites.
Presents the intuitions behind the methods.

[Ogi Ogas · Rating 4 out of 5]

My ratings of books on Goodreads are solely a crude ranking of their utility to me, and not an evaluation of literary merit, entertainment value, social importance, humor, insightfulness, scientific accuracy, creative vigor, suspensefulness of plot, depth of characters, vitality of theme, excitement of climax, satisfaction of ending, or any other combination of dimensions of value which we are expected to boil down through some fabulous alchemy into a single digit.

[George · Rating 1 out of 5]

This book is very out of date - it was written well before the deep learning revolution.

Like a physics book about the luminiferous aether, the only people who should read this book are historians of science and technology. They will wonder how mistaken we once were about the power and potential of deep neural networks.

[DJ]

intro to neural networks