Massively Deep Artificial Neural Networks for Handwritten Digit Recognition
classification
💻 cs.CV
cs.LGcs.NE
keywords
handwrittenrateachieveartificialboltzmanncardconsistingdatabase
read the original 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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