Deep Learning Classifiers with Memristive Networks: Theory and Applications (Modeling and Optimization in Science and Technologies, 14)

ISBN 10: 3030145220 / ISBN 13: 9783030145224

Edité par Springer, 2019

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New.

N° de réf. du vendeur ERICA77330301452206

Détails bibliographiques

• Titre: Deep Learning Classifiers with Memristive Networks: Theory and Applications (Modeling and Optimization in Science and Technologies, 14)
• Éditeur: Springer
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 3030145220
• ISBN à 13 chiffres: 9783030145224
• Reliure: Hardcover
• État de l'article: New
• Type de livre: book

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Synopsis

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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