Summary
In this chapter, we discussed approaches for building a fraud detector for credit card transactions in the desperate case when no, or almost no, examples of the fraud class are available. This solution trains a neural autoencoder to reproduce legitimate transactions from the input onto the output layer. Some postprocessing is necessary to set an alarm for the fraud candidate based on the reconstruction error.
In describing this solution, we have introduced the concept of training and deployment applications, components, optimization loops, and switch blocks.
In the next chapter, we will discuss a special family of neural networks, so-called recurrent neural networks, and how they can be used to train neural networks for sequential data.
Questions and Exercises
Check your level of understanding of the concepts presented in this chapter by answering the following questions:
- What is the goal of an autoencoder during training?
a) To reproduce the input to the...
