Massively Deep Artificial Neural Networks for Handwritten Digit Recognition
نویسنده
چکیده
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. Keywords—ANN (Artificial Neural Networks), RBM (Restrictive Boltzmann Machine), MNIST handwritten database, GPU (Graphics Processing Unit)
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ورودعنوان ژورنال:
- CoRR
دوره abs/1507.05053 شماره
صفحات -
تاریخ انتشار 2015