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Deep learning

Nature volume 521, pages 436–444 (2015)Cite this article

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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Acknowledgements

The authors would like to thank the Natural Sciences and Engineering Research Council of Canada, the Canadian Institute For Advanced Research (CIFAR), the National Science Foundation and Office of Naval Research for support. Y.L. and Y.B. are CIFAR fellows.

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Authors and Affiliations

  1. Facebook AI Research, 770 Broadway, New York, 10003, New York, USA

    Yann LeCun

  2. New York University, 715 Broadway, New York, 10003, New York, USA

    Yann LeCun

  3. Department of Computer Science and Operations Research Université de Montréal, Pavillon André-Aisenstadt, PO Box 6128 Centre-Ville STN, Montréal, H3C 3J7, Quebec, Canada

    Yoshua Bengio

  4. Google, 1600 Amphitheatre Parkway, Mountain View, 94043, California, USA

    Geoffrey Hinton

  5. Department of Computer Science, University of Toronto, 6 King's College Road, Toronto, M5S 3G4, Ontario, Canada

    Geoffrey Hinton

Corresponding author

Correspondence to Yann LeCun.

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

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