Convolution Neural Networks by Image Net

We trained a large, deep convolution neural network to classify the 1.2 million high-resolution images in the
Image Net LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5
error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural
network, which has 60 million parameters and 650,000 neurons, consists of five convolution layers, some of
which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way soft ax. To
make training faster, we used non-saturating neurons and a very efficient GPU implementation of the
convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed
regularization method called ―dropout‖ that proved to be very effective. We also entered a variant of this model
in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2%
achieved by the second-best entry .