Variational Auto Encoders (VAE) for the Numerai Dataset

The Numerai dataset contains decades of historical data on the global stock market. Machine learning models trained on the dataset learn to predict stock returns and earn cryptocurrency (NMR) based on performance in the Numerai Tournament. This blog post first explains "why" variational autoencoder is a suitable tool in a Numerai model developer stack. Then, we discuss "what" a variational autoencoder is and show "how" you can train one. We can use VAEs for anomaly detection, denoising, and generating synthetic data.