Deep Neural Network (DNN).

This is a graphical representation of a standard feedforward DNN architecture. The DNN is fed with an input vector x of dimension D which is transformed by the hidden layers hj (composed of Nj hidden units) according to an activation function g and the parameters of the DNN (weight matrices W and bias vectors b). Finally the output layer O produces the output of the DNN for the target task (for the case of classification, the posterior probability of an input vector to belong to each of the C classes). Reprinted from [19] under a CC BY license, with permission from Alicia Lozano et. al., original copyright 2017.