In this chapter, we are going to develop a Deep Neural Network (DNN) using the standard feedforward network architecture. We will add components and changes to the application while we progress through the recipes. Make sure to revisit Chapter 1, Introduction to Deep Learning in Java, and Chapter 2, Data Extraction, Transformation, and Loading, if you have not already done so. This is to ensure better understanding of the recipes in this chapter.
We will take an example of a customer retention prediction for the demonstration of the standard feedforward network. This is a crucial real-world problem that every business wants to solve. Businesses would like to invest more in happy customers, who tend to stay customers for longer periods of time. At the same time, predictions of losing customers will make businesses focus more...
