Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

Fast, flexible and easy to use probabilistic modelling in Python.

LibRec: A Leading Java Library for Recommender Systems, see

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Bayesian inference with probabilistic programming.

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

Sample code for the Model-Based Machine Learning book.

A domain-specific probabilistic programming language for scalable Bayesian data cleaning

A list of time-lasting classic books, which not only help you figure out how it works, but also grasp when it works and why it works in that way.

Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.

This project has two parts. In part one, we use markov random field to denoise an image. In Part two, we use similar model for image segmentation.

High-performance reactive message-passing based Bayesian inference engine

🌀 Stanford CS 228 - Probabilistic Graphical Models

Inference of microbial interaction networks from large-scale heterogeneous abundance data

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.

Official Repository of "Contextual Graph Markov Model" (ICML 2018 - JMLR 2020)

Separating Normalizing Flows code from Pyro and improving API

PyHGF: A neural network library for predictive coding