Deep, big, simple neural nets for handwritten digit recognition

Dan Claudiu Cireşan, Ueli Meier, Luca Maria Gambardella, Jürgen Schmidhuber

Research output: Contribution to journalArticlepeer-review

745 Scopus citations


Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning. © 2010 Massachusetts Institute of Technology.
Original languageEnglish (US)
Pages (from-to)3207-3220
Number of pages14
JournalNeural Computation
Issue number12
StatePublished - Dec 1 2010
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

  • Cognitive Neuroscience


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