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

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

This repository dives deeper than mere tactics. Here, you'll find a meticulously chosen selection of books, papers and courses which not only equip you with actionable knowledge but also illuminate the underlying principles and philosophies that make those strategies work.

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.

🌲 Stanford CS 228 - Probabilistic Graphical Models

High-performance reactive message-passing based Bayesian inference engine

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

PyHGF: A neural network library for predictive coding

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

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

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

General purpose C++ library for managing discrete factor graphs

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

MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation (MICCAI 2020)