  Activation functions play a key role in neural networks so it becomes
fundamental to understand their advantages and disadvantages in order to
achieve better performances. This paper will first introduce common types of
non linear activation functions that are alternative to the well known sigmoid
function and then evaluate their characteristics. Moreover deeper neural
networks will be analysed because they positively influence the final
performances compared to shallower networks. They also strictly depend on the
weight initialisation hence the effect of drawing weights from Gaussian and
uniform distribution will be analysed making particular attention on how the
number of incoming and outgoing connection to a node influence the whole
network.
