Deep Learning Classifiers with Memristive Networks : Theory and Applications /
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 lea...
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| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2020.
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| Edition: | 1st ed. 2020. |
| Series: | Modeling and Optimization in Science and Technologies,
14 |
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| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Physical Description: | 1 online resource (XIII, 213 pages 124 illustrations, 102 illustrations in color.) |
| ISBN: | 9783030145248 |
| ISSN: | 2196-7334 ; |
| DOI: | 10.1007/978-3-030-14524-8 |