Neural Networks for Pattern Recognition (Advanced Texts in Econometrics)
This book provides a solid statistical foundation for neural networks from a pattern recognition  perspective. The focus is on the types of neural nets that are most widely used in practical applications,  such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many  different types of neural networks, Bishop thoroughly covers topics such as density estimation, error  functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are  organized well and all mathematical foundations are explained before being applied to neural networks.  The text is suitable for a graduate or advanced undergraduate level course on neural networks or for  practitioners interested in applying neural networks to real-world problems. The reader is assumed to have  the level of math knowledge necessary for an undergraduate science degree.