- GP Regression: Introduction to Gaussian processes with a regression problem using PyMC3 PPL.
- MC Dropout: Uncertainty estimates usng dropout.
- Bayesian CNN: Implementing a Bayesian CNN for classification using variational inference and MNIST with Edward PPL. Includes uncertainty estimation on nMNIST.
- Bayesian Regression with Neural Networks: Non-linear regression with neural networks using Variational Inference. Compares working with VI in Edward and PyMC3 PPLs
-
Notifications
You must be signed in to change notification settings - Fork 1
shehel/Bayesian_Learning
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
Probabilistic programming - Bayesian deep networks and GPs
Topics
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published