The notes available online have have formed the basis of a book to be published later this year by UCL Press Ltd. UK. This book has been written in the same spirit as the notes in that basic concepts, techniques and ideas are given priority over lengthy mathematical proofs and technical detail. However, the book contains a significant increase in material, including sections on ART, RBFs, PCA, digital nodes, alternative views of network function, applications etc.

This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.

I told you AI would be coming to your cell phone soon. Not only do cell phones come with powerful processors now but there are
special circumstances that make cell phone AI both more practical and
more interesting.

The number of models available in neural network literature is quite large. Very often the treatment is mathematical and complex. This book provides illustrative examples in C++ that the reader can use as a basis for further experimentation. A key to learning about neural networks to appreciate their inner workings is to experiment. Neural networks, in the end, are fun to learn about and discover. Although the language for description used is C++, you will not find extensive class libraries in this book.