Monday, 21 October 2013

Neural Networks: A Comprehensive Foundation by Simon Haykin



Book Description
This book is for graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.
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.

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.

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