Preprint Article Version 1 This version is not peer-reviewed

Deep Learning: A Review

Version 1 : Received: 6 October 2018 / Approved: 10 October 2018 / Online: 10 October 2018 (11:37:13 CEST)

How to cite: Vargas, R.; Mosavi, A.; Ruiz, R. Deep Learning: A Review. Preprints 2018, 2018100218 (doi: 10.20944/preprints201810.0218.v1). Vargas, R.; Mosavi, A.; Ruiz, R. Deep Learning: A Review. Preprints 2018, 2018100218 (doi: 10.20944/preprints201810.0218.v1).

Abstract

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

Subject Areas

deep learning; machine learning; applied deep learning

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