Deep learning

Clicks: 585
ID: 109948
2015
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #8 of 375 articles by views in Nature

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 375 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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 ot …
Reference Key
y2015naturedeep Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors LeCun Y;Bengio Y;Hinton G;;
Journal Nature
Year 2015
DOI
DOI not found
URL
https://www.ncbi.nlm.nih.gov/pubmed/26017442
Keywords
neural networks Computer National Center for Biotechnology Information NCBI NLM MEDLINE review pubmed abstract nih national institutes of health national library of medicine computers pmid:26017442 doi:10.1038/nature14539 research support non-u.s. gov't u.s. gov't non-p.h.s. yann lecun yoshua bengio geoffrey hinton algorithms artificial intelligence* / trends language computer science mathematics and computing

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.