{"product_id":"deep-learning-classifiers-with-memristive-networks-theory-and-applications-9783030145224","title":"Deep Learning Classifiers with Memristive Networks: Theory and Applications","description":"\u003cp\u003e • Author(s): Alex Pappachen James\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Artificial Intelligence - Computer Vision \u0026amp; Pattern Recognit\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Hardcover","offer_id":45276031352983,"sku":"9783030145224","price":12354.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783030145224.webp?v=1769284530","url":"https:\/\/atlanticbooks.com\/products\/deep-learning-classifiers-with-memristive-networks-theory-and-applications-9783030145224","provider":"Atlantic Books","version":"1.0","type":"link"}