Machine Learning for Finance

Summary

In this chapter, you have learned about the two most important types of generative models: autoencoders and GANs. We first developed an autoencoder for MNIST images. We then used a similar architecture to encode credit card data and detect fraud. Afterward, we expanded the autoencoder to a VAE. This allowed us to learn distributions of encodings and generate new data that we could use for training.

Afterward, we learned about GANs, again first in the context of MNIST images and then in the context of credit card fraud. We used an SGAN to reduce the amount of data we needed to train our fraud detector. We used model outputs to reduce the amount of labeling necessary through active learning and smarter labeling interfaces.

We've also discussed ...