jiayong1/Rprop-Variants-Implementation-and-Performance-Comparison

The resilient back-propagation (Rprop), proposed by Riedmiller and Braun, is one of the most popular learning algorithms for neural networks in backpropagation. It overcomes the inherent disadvantages of pure gradient-descent by performing a local adaptation of the weight-updates according to the behavior of the error function.

Python