Principles of Artificial Neural Networks (Advanced Circuits and Systems)

Graupe, Daniel

ISBN 10: 9812706240 / ISBN 13: 9789812706249

Editore: World Scientific Publishing Company, 2007

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SHRINK-WRAPPED NEW: PRINCIPLES OF ARTIFICIAL NEURAL NTWORKS ED2 HARDCOVER 2007 Principles of Artificial Neural Networks (Advanced Circuits and Systems) 2nd ed. Edition by Daniel Graupe (Author) OUR REFERENCE: 150E1-SK4-HC-9812706240-HC-1P5-BluGrn-B-2007 DESCRIPTION The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks. Product details Publisher ‏ : ‎ World Scientific Publishing Company; 2nd ed. edition (April 1, 2007) Language ‏ : ‎ English Hardcover ‏ : ‎ 320 pages ISBN-10 ‏ : ‎ 9812706240 ISBN-13 ‏ : ‎ 9789812706249. Codice articolo D-150E1-SFK4-96240-HC-1P5-BluGrn-B

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Titolo: Principles of Artificial Neural Networks (...

Casa editrice: World Scientific Publishing Company

Data di pubblicazione: 2007

Legatura: Hardcover

Condizione: New

Edizione: 2nd Edition

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Riassunto:

The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strengths and weaknesses of the different networks.

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