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Massively Deep Artificial Neural Networks for Handwritten Digit Recognition
Keiron O'Shea · 2015-07-18 · via cs.NE updates on arXiv.org

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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