 It features inference, as well as the optimization concepts of training and testing, related to fitting and generalization, respectively.More specifically, the probabilistic interpretation considers the activation nonlinearity as a cumulative distribution function.The probabilistic interpretation led to the introduction of dropout as regularizer in neural networks.