Deep Learning Classifiers with Memristive Networks

Alex Pappachen James

ISBN 10: 3030145220 / ISBN 13: 9783030145224

Published by Springer International Publishing, Springer International Publishing Apr 2019, 2019

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.

Seller Inventory # 9783030145224

Bibliographic Details

• Title: Deep Learning Classifiers with Memristive Networks
• Publisher: Springer International Publishing, Springer International Publishing Apr 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 3030145220
• ISBN 13: 9783030145224
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 18D millimeters 241 millimeters width by 160 millimeters height by 18 millimeters depth
• Weight: 512 grams

About this title

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.

About the Author