Introduction
While neural networks have been around in some form since the mid-twentieth century, they have recently surged in popularity. Be it self-driving cars or healthcare technologies, neural networks are fundamental to some of the most innovative products being developed.
In this chapter, we will train a neural network to predict whether a loan applicant in the GermanCredit dataset has a good or bad credit rating. To do this, we will partition the dataset into a training set, a development set, and a validation set. The neural network will be trained on the training set, and we will evaluate whether it makes good predictions on the development and validation sets. We will use cross-validation, along with four different evaluation metrics, to select between different neural network architectures.