In this chapter, we introduced two unsupervised learning methods that leverage deep learning. Autoencoders learn sophisticated, nonlinear feature representations that are capable of significantly compressing complex data while losing little information. As a result, they are very useful to counter the curse of dimensionality associated with rich datasets that have many features, which is especially common in alternative data. We also saw how to implement various types of autoencoders using Keras.
Then, we covered GANs, which learn a probability distribution over the input data and are hence capable of generating synthetic samples that are representative of the target data. While there are many practical applications for this very recent innovation, they could be particularly valuable for algorithmic trading if the success in generating time-series training data in the...
