Deep Learning Classifiers with Memristive Networks : Theory and Applications / edited by Alex Pappachen James

Contributor(s):

James, Alex Pappachen [HerausgeberIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Modeling and Optimization in Science and Technologies ; 14 | Springer eBooks Intelligent Technologies and RoboticsPublisher: Cham : Springer, 2020Description: 1 Online-Ressource (XIII, 213 p. 124 illus., 102 illus. in color)ISBN:

9783030145248

Subject(s):

Additional physical formats: 9783030145224 | Erscheint auch als: 978-3-030-14522-4 Druck-AusgabeDDC classification:

006.3 23

LOC classification:

Q342

DOI: DOI: 10.1007/978-3-030-14524-8Online resources:

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 memristorsPPN: PPN: 1666735981Package identifier: Produktsigel: ZDB-2-INR | ZDB-2-SEB | ZDB-2-SXIT

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