Backpropagation - Theory, Architectures, and Applications (Paperback)

Composed of three sections, this book presents the most popular training algorithm for neural networks: backpropagation. The first section presents the theory and principles behind backpropagation as seen from different perspectives such as statistics, machine learning, and dynamical systems. The second presents a number of network architectures that may be designed to match the general concepts of Parallel Distributed Processing with backpropagation learning. Finally, the third section shows how these principles can be applied to a number of different fields related to the cognitive sciences, including control, speech recognition, robotics, image processing, and cognitive psychology. The volume is designed to provide both a solid theoretical foundation and a set of examples that show the versatility of the concepts. Useful to experts in the field, it should also be most helpful to students seeking to understand the basic principles of connectionist learning and to engineers wanting to add neural networks in general -- and backpropagation in particular -- to their set of problem-solving methods.

General

Imprint:	Psychology Press
Country of origin:	United States
Series:	Developments in Connectionist Theory Series
Release date:	February 1995
First published:	1995
Editors:	Yves Chauvin • David E. Rumelhart
Dimensions:	229 x 152 x 35mm (L x W x T)
Format:	Paperback
Pages:	576
ISBN-13:	978-0-8058-1259-6
Categories:	Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > General Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	0-8058-1259-8
Barcode:	9780805812596

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!