Bishop C.M. Neural Networks for Pattern Recognition

Oxford: Clarendon Press, 1995. — 498 p.Bishop is a leading researcher who has a deep understanding of the material and has gone to great lengths to organize it into a sequence that makes sense. He has wisely avoided the temptation to try to cover everything and has therefore omitted interesting topics like reinforcement learning, Hopfield Networks and Boltzmann machines in order to focus on the types of neural network that are most widely used in practical applications. He assumes that the reader has the basic mathematical literacy required for an undergraduate science degree, and using these tools he explains everything from scratch. Before introducing the multilayer perceptron, for example, he lays a solid foundation of basic statistical concepts. So the crucial concept of overfitting is first introduced using easily visualised examples of one-dimensional polynomials and only later applied to neural networks. An impressive aspect of this book is that it takes the reader all the way from the simplest linear models to the very latest Bayesian multilayer neural networks without ever requiring any great intellectual leaps.Statistical Pattern Recognition
Probability Density Estimation
Single-Layer Networks
The Multi-layer Perceptron
Radial Basis Functions
Error Functions
Parameter Optimization Algorithms
Pre-processing and Feature Extraction
Learning and Generalization
Bayesian Techniques