- Neural networks a comprehensive foundation haykin training
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- Neural networks and learning machines simon haykin
- Neural networks a comprehensive foundation
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Although the book is very complete, it is also mathematically rigorous. To really understand it from cover to cover you would need to know - both conceptually and practically - calculus, linear algebra, adaptive signal processing, and dynamical systems, since this book assumes you already know these subjects and makes heavy use of their properties. Fortunately, to get a good basic understanding of what neural networks are and what they can accomplish, you won't need to understand the entire book. I found chapters 1-7 to be fairly accessible and self-contained. It is only once you get past the subject of supervised learning in chapter eight that the mathematics and the book get particularly difficult. Another problem with the book is that it abruptly goes from a forest to a trees viewpoint of neural networks. It will be working along in a very theoretical manner for some number of pages, when suddenly, out of nowhere, it will mention something practical or show an example that clarifies a great deal.
Neural networks a comprehensive foundation haykin training
Main Neural networks: a comprehensive foundation 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 at: 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. ISBN 13: 978-0-13-147139-9 You may be interested in Powered by Rec2Me Most frequently terms
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Thu, 28 Jan 2021 17:35:42 +0000