The effort to build machines that are able to learn and undertake tasks such as datamining, image processing and pattern recognition has led to the development of artificial neural networks in which learning from examples may be described and understood. The contribution to this subject made over the past decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics, and include many examples and exercises.

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Learning is natural activity, and it has always been a challenge for us to understand the process. Artificial neural networks provide a simple framework in which learning from examples may be described and understood. The authors provide a coherent account of various important concepts and techniques of statistical mechanics and their application to learning theory, supplement this with background material in mathematics and physics and include many examples and exercises to make a book that can be used with courses, or for self-teaching, or as a handy reference.

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