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Massively Deep Artificial Neural Networks for Handwritten Digit Recognition
Published 17 Jul 2015 in cs.CV, cs.LG, and cs.NE | (1507.05053v1)
Abstract: Greedy Restrictive Boltzmann Machines yield an fairly low 0.72% error rate on the famous MNIST database of handwritten digits. All that was required to achieve this result was a high number of hidden layers consisting of many neurons, and a graphics card to greatly speed up the rate of learning.
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