Here it is! We've finally gotten to the fun stuff! In this chapter, we will be creating a deep neural network that can classify an observation into multiple classes, and this is one of those places where neural networks really do well. Let's talk just a bit more about the benefit of deep neural networks for this class of problems.
Just so we're all talking about the same thing, let's define multiclass classification before we begin. Imagine we had a classifier that had, as inputs, the weights of various fruits and would predict the fruit given the weight. The output might be exactly one class in a set of classes (apple, banana, mango, and so on). That's multiclass classification, not to be confused with multilabel, which is the situation where a model might predict whether or not a set of labels will apply to the observations that aren't mutually exclusive.