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[null,null,"Titolo",null,"Deep Learning Classifiers with Memristive Networks : Theory and Applications / / edited by Alex Pappachen James"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"3-030-14524-7"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (216 pages)"]
[null,null,"Collana",null,"Modeling and Optimization in Science and Technologies, , 2196-7326 ; ; 14"]
[null,null,"Disciplina",null,"006.32"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Pattern perception"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Optical data processing"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Pattern Recognition"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Image Processing and Computer Vision"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,null,"Sommario/riassunto","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."]