What is the connection between KL divergence and variational inference in deep learning?

KL divergence is used to approximate intractable posteriors in probabilistic models.

Variational inference minimizes KL divergence between an approximate distribution and true posterior. This transforms inference into optimization. In deep learning, this enables scalable probabilistic modeling like VAEs where exact inference is impossible due to high-dimensional integrals.