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 In real-world applications, the posterior over the latent variables Z given some data D is usually intractable. But we can use a ... This short tutorial covers the basics of
MLFoundations #Calculus #MachineLearning In this video, we use a hands-on code demo in
Summary & Highlights for Variational Inference By Automatic Differentiation In Tensorflow Probability
- 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 this video you will learn everything about
- David Blei, Rajesh Ranganath, Shakir Mohamed. One of the core problems of modern statistics and machine learning is to ...
- Inference of probabilistic models using
- This is the twentyfourth lecture in the Probabilistic ML class of Prof. Dr. Philipp Hennig, updated for the Summer Term 2021 at the ...
That wraps up our extensive overview of Variational Inference By Automatic Differentiation In Tensorflow Probability.
