Defining feedforward networks
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 which represent them. These functions are composed in a directed acyclic graph.
The model is associated with a directed acyclic graph describing how the functions are composed together. For example, there are three functions f(1), f(2), and f(3) connected...
