[vermarish]

At a high level, Bayesian statistics and DL share the same objective of fitting parameters to models.

In particular, variational inference is a family of techniques that makes these kinds of problems computationally tractable. It shows up everywhere from variational autoencoders, to time-series state-space modeling, to reinforcement learning.

If you want to learn more, I recommend reading Murphy's textbooks on ML: https://probml.github.io/pml-book/book2.html