Есть множество книг, которые дают хорошее, глубокое описание предмета. Среди них мы, в частности, рекомендуем:

Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford: University Press. Extremely well-written, up-to-date. Requires a good mathematical background, but rewards careful reading, putting neural networks firmly into a statistical context.

Carling, A. (1992). Introducing Neural Networks. Wilmslow, UK: Sigma Press. A relatively gentle introduction. Starting to show its age a little, but still a good starting point.

Fausett, L. (1994). Fundamentals of Neural Networks. New York: Prentice Hall. A well-written book, with very detailed worked examples to explain how the algorithms function.

Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. New York: Macmillan Publishing. A comprehensive book, with an engineering perspective. Requires a good mathematical background, and contains a great deal of background theory.

Patterson, D. (1996). Artificial Neural Networks. Singapore: Prentice Hall. Good wide-ranging coverage of topics, although less detailed than some other books.

Ripley, B.D. (1996). Pattern Recognition and Neural Networks. Cambridge University Press. A very good advanced discussion of neural networks, firmly putting them in the wider context of statistical modeling.

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