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

 
9783030145224: Deep Learning Classifiers with Memristive Networks: Theory and Applications: 14 (Modeling and Optimization in Science and Technologies)
Tapa dura
ISBN 10: 3030145220 ISBN 13: 9783030145224
Editorial: Springer, 2019

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.

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De la contraportada

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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Editorial
Springer
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3030145220
ISBN 13 
9783030145224
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
228
Editor
James Alex Pappachen
Contacto del fabricante
no disponible
Persona responsable
CMN Printpool Inh. Mathis Neumann
https://www.also.com/ec/cms5/en_6000/6000/index.jsp

Friedrich-Karl-Str. 9
Hamburg
Alemania

Otras ediciones populares con el mismo título

9783030145231: Deep Learning Classifiers with Memristive Networks: Theory and Applications

Edición Destacada

ISBN 10:  3030145239 ISBN 13:  9783030145231
Editorial: Springer, 2019
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