Deep Probabilistic Programming with Pyro


In this talk, we will learn about Pyro ( a PPL built on PyTorch. We will discuss what probabilistic programming is, and how we can integrate it with deep learning to tackle open machine learning problems in generative modeling. We will talk about approximate inference techniques such as variational inference, and walk through some of the tools and examples to make inference on models automatic. If you are a data scientist, an ML engineer, or an ML researcher, this talk will be of interest to you!

Background Knowledge
Pytorch, Python


JP is a research scientist at Facebook where he works on probabilistic programming, approximate inference, and Bayesian nonparametrics. He is a founding coauthor of the probabilistic programming language Pyro. The main question that guides his research is: how do we build and perform inference on models in an automatic yet principled way? Prior to Facebook, he was at Uber AI Labs working at the intersection of deep learning and statistics, focusing on time series forecasting and mapping for self driving cars.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from Youtube
Consent to display content from Vimeo
Google Maps
Consent to display content from Google