TECHNICAL FIELD

The present disclosure relates generally to deep learning and more specifically to deep learning using a hybrid approach.

BACKGROUND

Deep learning on data sets is a valuable approach to learning the generalized nature of the data in the data sets. Many approaches to this problem utilize a neural network that is trained against a set of training data and then tested against a set of testing data. The neural network is trained by utilizing a cost function whose gradient is determined over an epoch in which each of the elements in the set of training data is evaluated by the neural network. The gradient is then used to update the weights used by the neurons in the neural network before the training data is presented to the neural network again, the gradient is re-determined, and the weights are updated again. This process continues until the neural network converges to a steady state (e.g., where the cost function is minimized) and/or the error rate for the testing data meets an accuracy criterion. The ability of the neural network to rapidly converge to a solution (e.g., in a reasonable number of epochs) may vary depending upon the data in the data sets, the learning rule used to adapt the weights based on the gradient, various scaling factors, learning rates, and/or the like.

Accordingly, it would be advantageous to have systems and methods for improving training of deep networks.

