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{ "item_title" : "Deep Learning Classifiers with Memristive Networks", "item_author" : [" Alex Pappachen James "], "item_description" : "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. ", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/3/03/014/522/3030145220_b.jpg", "price_data" : { "retail_price" : "199.99", "online_price" : "199.99", "our_price" : "199.99", "club_price" : "199.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Deep Learning Classifiers with Memristive Networks|Alex Pappachen James

Deep Learning Classifiers with Memristive Networks : Theory and Applications

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Overview

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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Details

  • ISBN-13: 9783030145224
  • ISBN-10: 3030145220
  • Publisher: Springer
  • Publish Date: April 2019
  • Dimensions: 9.21 x 6.14 x 0.56 inches
  • Shipping Weight: 1.1 pounds
  • Page Count: 213

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