This key "user-friendly" feature notwithstanding, the book provides
a full level of explanation of the technical aspects of the
subject, which non-mathematical rivals usually fail to provide,
thereby leaving those areas obscure. Although the study of neural
networks is underpinned by ideas that are often best described
mathematically, the fundamentals of the subject are accessible
without the full mathematical apparatus, as this treatment amply
demonstrates. The book provides comprehensive coverage of the
following key areas: artificial neurons as models of their real
counterparts; the geometry of network action in pattern space;
gradient descent methods, including back-propagation; associative
memory and Hopfield nets; and self-organization and feature maps.
The traditionally difficult topic of adaptive resonance theory is
clarified within a hierarchical description of its operation, which
disentangles features specific to separate levels of discussion.
Finally, a chapter is devoted to organizing the study of neural
networks in various ways, and it attempts to overcome the general
impression that it is a loose-knit collection of structures and
recipes. The primary aim of the book
General
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