The goal of a neural network model is to approximate some function $f^*$. For example, for a classifier, $y = f^*(x)$ maps an input $x$ to a category $y$. A neural network model defines a mapping $y = f (x; \theta)$ and learns the values for the parameter $\theta$ that result in the best function approximation.