title: Deep, big, simple neural nets for handwritten digit recognition.
abstract: 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.
authors.affiliation: IDSIA, 6928 Manno-Lugano, Switzerland. dan@idsia.ch
authors.forename: Dan Claudiu
authors.initials: DC
authors.lastname: Cireşan
authors.forename: Ueli
authors.initials: U
authors.lastname: Meier
authors.forename: Luca Maria
authors.initials: LM
authors.lastname: Gambardella
authors.forename: Jürgen
authors.initials: J
authors.lastname: Schmidhuber
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006236:Handwriting; D010363:Pattern Recognition, Automated / Q000379:methods*