{"product_id":"9783030145224","title":"Deep Learning Classifiers with Memristive Networks","description":"\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\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePublication Year: \u003c\/strong\u003e2020\u003cbr\u003e\u003cstrong\u003eImprint: \u003c\/strong\u003eSpringer International Publishing\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003eFormat: H\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003eWeight (Gram): 512\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e","brand":"Alex Pappachen James","offers":[{"title":"Default Title","offer_id":41277663838386,"sku":"9783030145224","price":249.59,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0502\/5382\/4178\/products\/3030145220.01_SCLZZZZZZZ.jpg?v=1636202509","url":"https:\/\/readabook.store\/products\/9783030145224","provider":"READABOOK BY ALKEM","version":"1.0","type":"link"}