Deep, Big, Simple Neural Nets for Handwritten Digit Recognition

@article{Ciresan2010DeepBS,
  title={Deep, Big, Simple Neural Nets for Handwritten Digit Recognition},
  author={Dan C. Ciresan and U. Meier and L. Gambardella and J. Schmidhuber},
  journal={Neural Computation},
  year={2010},
  volume={22},
  pages={3207-3220}
}
  • Dan C. Ciresan, U. Meier, +1 author J. Schmidhuber
  • Published 2010
  • Computer Science, Medicine
  • Neural Computation
  • 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. 
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