Building and Training the Autoencoder

Let's go into detail about the particular application we will build to tackle fraud detection with a neural autoencoder. Like all data science projects, it includes two separate applications: one to train and optimize the whole strategy on dedicated datasets, and one to set it in action to analyze real-world credit card transactions. The first application is implemented with the training workflow; the second application is implemented with the deployment workflow.

Tip

Often, training and deployment are separate applications since they work on different data and have different goals.

The training workflow uses a lab dataset to produce an acceptable model to implement the task, sometimes requiring a few different trials. The deployment workflow does not change the model or the strategy anymore; it just applies it to real-world transactions to get fraud alarms.

In this section, we will focus on the training phase, including the following...