Deep Learning: Current State

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Deep learning, a derived from machine learning, has grown into widespread usage with applications as diverse as cancer detection, elephant spotting, and game development. The number of published studies shows an increasing interest by researchers because of its demonstrated ability to achieve high performance in the solution of complex problems, the wide availability of data and computing resources, and the groundbreaking development of effective algorithms. This paper reviews the current state of deep learning. It includes a revision of basic concepts, such as the operations of feed forward and backpropagation, the use of convolution to extract features, the role of the loss function, and the optimization and learning processes; the survey of main stream techniques, in particular convolutional, recurrent, recursive, deep belief, deep generative, generative adversarial, and variational auto-enconder neural networks; the description of an ample array of applications organized by the type of technique employed; and the discussion of some of its most intriguing open problems.