  The training of deep neural nets is expensive. We present a predictor-
corrector method for the training of deep neural nets. It alternates a
predictor pass with a corrector pass using stochastic gradient descent with
backpropagation such that there is no loss in validation accuracy. No special
modifications to SGD with backpropagation is required by this methodology. Our
experiments showed a time improvement of 9% on the CIFAR-10 dataset.
