Contouring learning rate to optimize neural nets

Check out Siddha Ganju's talk on embedded deep learning at the Artificial Intelligence Conference in San Francisco, Sept. 17-20, 2017. Learning rate is the rate at which the accumulation of information in a neural network progresses over time. The learning rate determines how quickly (and whether at all) the network reaches the optimum, most conducive location in the network for the specific output desired. In plain Stochastic Gradient Descent (SGD), the learning rate is not related to the shape of the error gradient because a global learning rate is used, which is independent of the error gradient. However, there are many modifications that can be made to the original SGD update rule that relates the learning rate to the magnitude and orientation of the error gradient.