Hands-On Machine Learning for Algorithmic Trading

Variational autoencoders

Variational Autoencoders (VAE) are more recent developments focused on generative modeling. More specifically, VAEs are designed to learn a latent variable model for the input data. Note that we encountered latent variables in Chapter 14, Topic Modeling.

Hence, VAEs do not let the network learn arbitrary functions as long as it faithfully reproduces the input. Instead, they aim to learn the parameters of a probability distribution that generates the input data. In other words, VAEs are generative models because, if successful, you can generate new data points by sampling from the distribution learned by the VAE.

The operation of a VAE is more complex than the autoencoders discussed so far and the details are beyond ...