Preface page ix 1 Getting Started 1.1 Artificial neural networks 1.2 A simple example 1.3 General setup 1.4 Problems 2 Perceptron Learning-Basics 2.1 Gibbs learning 2.2 The annealed approximation 2.3 The Gardner analysis 2.4 Summary 2.5 Problems 3 A Choice of Learning Rules 3.1 The Hebb rule 3.2 The perceptron rule 3.3 The pseudo-inverse rule 3.4 The adaline rule 3.5 Maximal stability 3.6 The Bayes rule 3.7 Summary 3.8 Problems 4 Augmented Statistical Mechanics Formulation 4.1 Maximal stabilities 4.2 Gibbs learning at non-zero temperature 4.3 General statistical mechanics formulation 4.4 Learning rules revisited 4.5 The optimal potential 4.

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