Deep Learning Classifiers with Memristive Networks : Theory and Applications / / edited by Alex Pappachen James

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (216 pages)
• Disciplina: 006.32
• Soggetto topico: Computational intelligence; Pattern perception; Data mining; Optical data processing; Computational Intelligence; Pattern Recognition; Data Mining and Knowledge Discovery; Image Processing and Computer Vision
• Persona (resp. second.): JamesAlex Pappachen
• Nota di bibliografia: Includes bibliographical references.
• 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.
• Titolo autorizzato: Deep Learning Classifiers with Memristive Networks
• ISBN: 3-030-14524-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910483938503321
• Lo trovi qui: Univ. Federico II
• Serie: Modeling and Optimization in Science and Technologies, . 2196-7326 ; ; 14