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|b Springer Nature
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|a Deep learning classifiers with memristive networks :
|b theory and applications /
|c Alex Pappachen James, editor.
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c [2020]
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| 300 |
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|a 1 online resource (xiii, 213 pages) :
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|a Modeling and optimization in science and technologies,
|x 2196-7326 ;
|v volume 14
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| 588 |
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|a Online resource; title from PDF title page (SpringerLink, viewed April 23, 2019).
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|a 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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|2 fast
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| 700 |
1 |
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|a James, Alex Pappachen,
|e editor.
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| 758 |
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|i has work:
|a Deep Learning Classifiers with Memristive Networks (Text)
|1 https://id.oclc.org/worldcat/entity/E39PD3d8P8b9V3h9cygpkPRbtX
|4 https://id.oclc.org/worldcat/ontology/hasWork
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0 |
8 |
|i Print version:
|t Deep learning classifiers with memristive networks.
|d Cham, Switzerland : Springer, [2020]
|z 3030145220
|z 9783030145224
|w (OCoLC)1084327574
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| 830 |
|
0 |
|a Modeling and optimization in science and technologies ;
|v v. 14.
|x 2196-7326
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-14524-8
|y Click for online access
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