Deep Learning Classifiers with Memristive Networks
Theory and Applications
(Sprache: Englisch)
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...
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Klappentext zu „Deep Learning Classifiers with Memristive Networks “
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.Inhaltsverzeichnis zu „Deep Learning Classifiers with Memristive Networks “
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Autoren-Porträt
Bibliographische Angaben
- 2019, 1st ed. 2020, XIII, 213 Seiten, 102 farbige Abbildungen, Masse: 16 x 24,1 cm, Gebunden, Englisch
- Herausgegeben: Alex Pappachen James
- Verlag: Springer, Berlin
- ISBN-10: 3030145220
- ISBN-13: 9783030145224
- Erscheinungsdatum: 17.04.2019
Sprache:
Englisch
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