Deep Learning Classifiers with Memristive Networks : Theory and Applications
Language: English
Published by Springer, 2019
Series: Book 10 of 14 - Modeling and Optimization in Science and Technologies
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 303.64
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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.
Seller Inventory # 9783030145224
- Title
- Deep Learning Classifiers with Memristive Networks : Theory and Applications
- Author
- Alex Pappachen James
- Publisher
- Springer
- Publication year
- 2019
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3030145220
- ISBN 13
- 9783030145224
- Item weight
- 512 grams
- Dimensions
- 241x160x18 mm
- Series
- Book 10 of 14: Modeling and Optimization in Science and Technologies
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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