Deep learning

Yann LeCun, Yoshua Bengio, Geoffrey Hinton

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)436-444
Number of pages9
JournalNature
Volume521
Issue number7553
DOIs
StatePublished - May 27 2015

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Learning
Drug Discovery
Genomics
Recognition (Psychology)
Datasets

ASJC Scopus subject areas

  • General
  • Medicine(all)

Cite this

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Deep learning. / LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey.

In: Nature, Vol. 521, No. 7553, 27.05.2015, p. 436-444.

Research output: Contribution to journalArticle

LeCun, Y, Bengio, Y & Hinton, G 2015, 'Deep learning', Nature, vol. 521, no. 7553, pp. 436-444. https://doi.org/10.1038/nature14539
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 27;521(7553):436-444. https://doi.org/10.1038/nature14539
LeCun, Yann ; Bengio, Yoshua ; Hinton, Geoffrey. / Deep learning. In: Nature. 2015 ; Vol. 521, No. 7553. pp. 436-444.
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