Deep Learning

Overview

Journal Nature

Specialty Science

Date 2015 May 29

PMID 26017442

Citations 8943

Authors

Yann LeCun

Yoshua Bengio

Geoffrey Hinton

Affiliations

Soon will be listed here.

Abstract

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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