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Deep Learning Classifiers with Memristive Networks : Theory and Applications - Alex Pappachen James

Deep Learning Classifiers with Memristive Networks

Theory and Applications

By: Alex Pappachen James (Editor)

eText | 8 April 2019

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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.

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Non-FictionEngineering & TechnologyTechnology in GeneralEngineering in GeneralComputing & I.T.Computer ScienceArtificial IntelligenceComputer VisionDatabasesData MiningGraphical & Digital Media ApplicationsPattern Recognition
Image ProcessingMathematicsProbability & StatisticsExpert Systems & Knowledge-Based Systems

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