UID:
almahu_9948130040102882
Format:
XIII, 213 p. 124 illus., 102 illus. in color.
,
online resource.
Edition:
1st ed. 2020.
ISBN:
9783030145248
Series Statement:
Modeling and Optimization in Science and Technologies, 14
Content:
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.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9783030145224
Additional Edition:
Printed edition: ISBN 9783030145231
Language:
English
DOI:
10.1007/978-3-030-14524-8
URL:
https://doi.org/10.1007/978-3-030-14524-8
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