Deep feedforward networks

So far in this chapter, we have done an in-depth visualization of the dataset, cleaned up the dataset by handling outliers, and also performed feature engineering to create useful features for our model. For the rest of the chapter, we'll talk about the architecture of deep feedforward neural networks, and we'll train one in Keras for a regression task.

Model architecture

In the previous chapter, Chapter 2, Predicting Diabetes with Multilayer Perceptrons, we used a relatively simple MLP as our neural network. For this project, since there are more features, we shall use a deeper model to account for the additional complexity. The deep feedforward network will have four hidden layers. The...