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 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 ...
PyData DC 2016 Jupyter notebook: https://nbviewer.jupyter.org/gist/AustinRochford/91cabfd2e1eecf9049774ce529ba4c16 ...
Summary & Highlights for Variational Inference By Automatic Differentiation In Tensorflow Probability
- Variational
- Inference of probabilistic models using
- This short tutorial covers the basics of
- TensorFlow Probability
- In this video you will learn everything about
That wraps up our extensive overview of Variational Inference By Automatic Differentiation In Tensorflow Probability.