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

James, Alex Pappachen [Editor]

ISBN 10: 3030145220 ISBN 13: 9783030145224

Editorial: Springer, 2019

Idioma: Ingl�s

Usado Condici�n: Clean & Unmarked Encuadernaci�n de tapa dura

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Descripci�n

A very clean and straight copy. Clean text and an uncreased spine. Nearly new. Tight. Sharp. 213 pp.

N� de ref. del art�culo 250603007

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Detalles bibliogr�ficos

T�tulo

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

Editor

Springer

Fecha de publicaci�n

2019

Idioma

Ingl�s

ISBN 10

3030145220

ISBN 13

9783030145224

Encuadernaci�n

Hardcover

Estado

Clean & Unmarked

Cat�logos de vendedores

Science

Acerca de

Sinopsis

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.