Acknowledgments to Addison-Wesley. Please read.

Preface

ONE Introduction

1.1/P 1.2/P 1.3/P 1.4/P 1.5/P 1.6/P 1.7/P 1.8/P

TWO The Hopfield Model

2.1 Associative Memories and Energy Function: 2.1/P 2.2/P 2.3/P 2.4/P 2.5/P 2.6/P

2.2 Hebb Rule and Capacity: 2.7/P 2.8/P 2.9/P 2.10/P 2.11/P 2.12/P 2.13/P 2.14/P 2.15/P 2.16/P 2.17/P 2.18/P

2.3 Stochastic Networks: 2.19/P 2.20/P 2.21/P 2.22/P

THREE Extensions of the Hopfield Model

3.1 Continuous-Valued Units: 3.1/P 3.2/P 3.3/P 3.4/P

FOUR Optimization Problems

4.1 Mapping Problems to Hopfield Network: 4.1/P 4.2/P 4.3/P 4.4/P

4.2 The Weighted Matching Problem: 4.5/P 4.6/P 4.7/P 4.8/P 4.9/P 4.9b/P 4.9c/P 4.9d/P

4.3 Graph Bipartitioning: 4.10/P 4.11/P 4.12/P 4.13/P 4.14/P 4.14b/P 4.14c/P

FIVE Simple Perceptrons

5.1 Feed-Forward Networks: 5.1/P 5.2/P 5.3/P 5.4/P 5.5/P 5.6/P

5.2 Threshold Units: 5.7/P 5.8/P 5.9/P 5.10/P 5.11/P 5.12/P 5.12b/P 5.13/P 5.14/P 5.15/P 5.16/P 5.17/P 5.18/P 5.19/P 5.20/P 5.21/P 5.22/P

5.3 Perceptron Learning Rule Proof of Convergence: 5.24/P 5.25/P 5.26/P

5.4 Continuous Units: 5.27/P 5.28/P 5.29/P 5.30/P

5.5 Capacity of the Simple Perceptron: 5.31/P 5.32/P 5.33/P 5.34/P 5.35/P

SIX Multi-Layer Networks

6.1 Back-Propagation: 6.1/P 6.2/P 6.3/P 6.4/P 6.5/P 6.6/P 6.7/P 6.8/P 6.9/P 6.10/P

6.2 Variations on Back-Propagation: 6.11/P 6.12/P 6.13/P

6.3 Examples and Applications: 6.14/P 6.15/P 6.16/P 6.17/P 6.18/P 6.19/P 6.20/P 6.21/P 6.22/P