Deep Learning Classifiers with Memristive Networks - Theory and Applications (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Modeling and Optimization in Science and Technologies, 14
Release date:	April 2019
First published:	2020
Editors:	Alex Pappachen James
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	213
Edition:	1st ed. 2020
ISBN-13:	978-3-03-014522-4
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks Promotions
LSN:	3-03-014522-0
Barcode:	9783030145224

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