Deep Learning Classifiers with Memristive Networks: Theory and Applications

James, Alex Pappachen (Edited by)

ISBN 10: 3030145220 / ISBN 13: 9783030145224

Editore: Springer, 2019

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228 pages. 9.25x6.10x0.67 inches. In Stock.

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Dati bibliografici

• Titolo: Deep Learning Classifiers with Memristive Networks: Theory and Applications
• Editore: Springer
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030145220
• ISBN 13: 9783030145224
• Rilegatura: Hardcover
• Condizione: Brand New

Descrizione articolo

Riassunto

This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.

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