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Deep neural networks and applications makes the readers aware about the various Artificial Neutral Networks (ANN) and the topologies related to Main Neutral Networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk management. It further elaborates on the Artificial Neutral Networks in detail and discusses the practical applications of the deep neutral networks. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research framework, development of the…mehr

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Produktbeschreibung
Deep neural networks and applications makes the readers aware about the various Artificial Neutral Networks (ANN) and the topologies related to Main Neutral Networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk management. It further elaborates on the Artificial Neutral Networks in detail and discusses the practical applications of the deep neutral networks. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research framework, development of the algorithms that quicken the data processing over complex network architectures and the optimization of database query structures using deep learning.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Autorenporträt
Ivan Stanimirovic gained his PhD from University of Nis, Serbia in 2013. His work spans from multi-objective optimization methods to applications of generalized matrix inverses in areas such as image processing and computer graphics and visualisations. He is currently working as an Assistant professor at Faculty of Sciences and Mathematics at University of Nis on computing generalized matrix inverses and its applications.