Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals
without the full mathematical apparatus. All aspects of the field are tackled, including 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.

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