Introduction to Variational Inference By Automatic Differentiation In Tensorflow Probability
Let's dive into the details surrounding Variational Inference By Automatic Differentiation In Tensorflow Probability. We find a surrogate posterior by maximizing the Evidence Lower Bound (ELBO). With a proposal distribution, this can be solved ...
Variational Inference By Automatic Differentiation In Tensorflow Probability Comprehensive Overview
In this video, we break down This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), check ... In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ...
This is the twentyfourth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the ...
Summary & Highlights for Variational Inference By Automatic Differentiation In Tensorflow Probability
- This short tutorial covers the basics of
- Inference of probabilistic models using
- TensorFlow Probability
- Variational Inference
- MLFoundations #Calculus #MachineLearning In this video, we use a hands-on code demo in
That wraps up our extensive overview of Variational Inference By Automatic Differentiation In Tensorflow Probability.
