For those entering the field of artificial neural networks, there has been an acute
need for an authoritative textbook that explains the main ideas clearly and consistently
using the basic tools of linear algebra, calculus, and simple probability
theory. There have been many attempts to provide such a text, but until now,
none has succeeded. Some authors have failed to separate the basic ideas and
principles from the soft and fuzzy intuitions that led to some of the models as
well as to most of the exaggerated claims. Others hav
e been unwilling to use the
basic mathematical tools that are essential for a rigorous understanding of the
material. Yet others have tried to cover too many different kinds of neural network
without going into enough depth on any one of them. The most successful
attempt to date has been "Introduction to the Theory of Neural Computation"
by Hertz, Krogh and Palmer. Unfortunately, this book started life as a graduate
course in statistical physics and it shows. So despite its many admirable qualities
it is not ideal as a general textbook.