Hands-On Machine Learning for Algorithmic Trading

How generative and discriminative models differ

The goal of generative models is to produce complex output, such as realistic images, given simple input, which can even be random numbers. They achieve this by modeling a probability distribution over the possible output. This probability distribution can have many dimensions, for example, one for each pixel in an image or its character or token in a document. As a result, the model can generate output that are very likely representative of the class of output. In this context, we can refer Richard Feynman's quote:

"What I cannot create, I do not understand."

This is often used to emphasize that modeling generative distributions is an important step toward more general AI and resembles human ...