Deep Learning Classifiers with Memristive Networks / Modeling and Optimization in Science and Technologies Bd.14 (PDF)
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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.
- 2019, 1st ed. 2020, 213 Seiten, Englisch
- Herausgegeben: Alex Pappachen James
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3030145247
- ISBN-13: 9783030145248
- Erscheinungsdatum: 08.04.2019
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Grösse: 9.27 MB
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