Although mathematical ideas underpin the study of neural networks, this book presents the fundamentals without the full mathematical apparatus. The author tackles virtually all aspects of the field, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods; associative memory and Hopfield nets; and self-organization and feature maps. The book provides a concrete focus through several real-world examples. This feature broadens the book's audience to include both students and professionals in cognitive science, psychology, and computer science as well as those involved in the design, construction, and management of networks.