Neural Networks: A Comprehensive Foundation – Simon Haykin – 2nd Edition

Description

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

Considers recurrent networks, such as Hopfield networks, Boltzmann , and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics.

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Table of Contents


1. Introduction.
2. Learning Processes.
3. Single-Layer Perceptrons.
4. Multilayer Perceptrons.
5. Radial-Basis Function Networks.
6. Support Vector Machines.
7. Committee Machines.
8. Principal Components Analysis.
9. Self-Organizing Maps.
10. Information-Theoretic Models.
11. Stochastic Machines & Their Approximates Rooted in Statistical Mechanics.
12. Neurodynamic Programming.
13. Temporal Processing Using Feedforward Networks.
14. Neurodynamics.
15. Dynamically Driven Recurrent Networks.
Epilogue.
Bibliography.
Index.
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