Probabilistic Programming with Tensorflow Probability
Probabilistic programming is a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. It represents an attempt to unify probabilistic modeling and traditional general-purpose programming in order to make the former easier and more widely applicable. The session will be a beginner-friendly tutorial on probabilistic programming using Tensorflow Probability (TFP).
- Philosophy of Bayesian Inference (3 mins)
- Setup/Installation for Tensorflow Probability (2 mins)
- Probabilistic programming in action (15 mins)
- Explaining posterior probability with example of coin toss (5 mins)
- Real world example of building Bayesian model (10 mins)
- Epilogue: current applications and limitations (2 mins)
- Questions and Answers (QNA) (3 mins)
- Familiarity with python syntax
- Google Colab
Hi, I am Anmol Jindal, I work as a data scientist at Intelligence Node (Mumbai). I have worked on Computer Vision, AI-powered Search engines and some other fun stuff. I have previously presented at Pycon India 2020.