Learning in Large Neural Networks

摘要

We will address here the simulation of large neural networks applied to real-world problems. In particular, we will consider the Multi Layer Perceptron (MLP) network and the back-propagation (BP) learning algorithm, showing that an efficient learning in MLP-BP networks depends on two factors: a fast BP algorithm and an efficient implementation respect to the particular target architecture. Unfortunately, as described here, the two objectives are mutually exclusive.