Neural Networks and Learning Machines: A Comprehensive Foundation

by Simon Haykin

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.

 

Neural Networks and Learning Machines, Third Edition is 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. This is ideal for professional engineers and research scientists.

 

Matlab codes used for the computer experiments in the text are available for download

 

Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.

Genres: Artificial Intelligence, Computer Science, Programming

936 pages, Kindle Edition

First published September 13, 1993

About the author

Simon Haykin was a Canadian electrical engineer noted for his pioneering work in Adaptive Signal Processing with emphasis on applications to Radar Engineering and Telecom Technology. He was a Distinguished University Professor at McMaster University in Hamilton, Ontario, Canada.

Reviews

[Shyam Poovaiah · Rating 4 out of 5]

A good ML book for those interested in Mathematical Proofs and completeness.

[zedoul · Rating 2 out of 5]

Recommendable. It concentrates on Neural network, including various approaches that I did not considered as Neural network - reinforcement learning, Kalman filter for instance. Explanations are relatively good.