Fast Training Algorithms for Feed Forward Neural Networks - Tawfiq, Luma N. M.; Oraibi, Yaseen A. (Ibn AL-Haitham Journal For Pure and Applied Sciences, 2017)
The aim of this paper, is to discuss several high performance training algorithms fall into two main categories. The first category uses heuristic techniques, which were developed from an analysis of the performance of the standard gradient descent algorithm. The second category of fast algorithms uses standard numerical optimization techniques such as: quasi-Newton . Other aim is to solve the drawbacks related with these training algorithms and propose an efficient training algorithm for FFNN