Deep feedforward networks, also called feedforward neural networks, are sometimes also referred to as Multilayer Perceptrons (MLPs). The goal of a feedforward network is to approximate the function of f∗. For example, for a classifier, y=f∗(x) maps an input x to a label y. A feedforward network defines a mapping from input to label y=f(x;θ). It learns the value of the parameter θ that results in the best function approximation.
We discuss RNNs in Chapter 5, Recurrent Neural Networks. Feedforward networks are a conceptual stepping stone on the path to recurrent networks, which power many natural language applications. Feedforward neural networks are called networks because they compose together many different functions ...
