Readership: Graduates and researchers in physics, mathematics, computer science, and other quantitative disciplines.
"The book provides an excellent class-tested material for graduate courses in artificial neural networks. It is completely self-contained and includes also thorough introduction to the discussed discipline-specific areas of mathematics...Therefore, this book represents a good reference source of applicable ideas for a wide audience including students, researchers and application specialists as well." - EMS Newsletter
I Introduction to Neural Networks 1: General introduction 2: Layered networks 3: Recurrent networks with binary neurons II Advanced Neural Networks 4: Competitive unsupervised learning processes 5: Bayesian techniques in supervised learning 6: Gaussian processes 7: Support vector machines for binary classification III Information Theory and Neural Networks 8: Measuring information 9: Identification of entropy as an information measure 10: Building blocks of Shannon's information theory 11: Information theory and statistical inference 12: Applications to neural networks IV Macroscopic Analysis of Dynamics 13: Network operation: macroscopic dynamics 14: Dynamics of online learning in binary perceptrons 15: Dynamics of online gradient descent learning V Equilibrium Statistical Mechanics of Neural Networks 16: Basics of equilibrium statistical mechanics 17: Network operation: equilibrium analysis 18: Gardner theory of task realizability Appendices A: Historical and bibliographical notes B: Probability theory in a nutshell C: Conditions for central limit theorem to apply D: Some simple summation identities E: Gaussian integrals and probability distributions F: Matrix identities G: The delta-distribution H: Inequalities based on convexity I: Metrics for parametrized probability distributions J: Saddle-point integration References
