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

LibRec: A Leading Java Library for Recommender Systems, see

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

Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) 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 list of classic books make better you understand not only how it works, but why it works.

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.

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

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

Official repository of the Contextual Graph Markov Model (ICML 2018 - JMLR 2020)

Blang's software development kit

Bayesian nonparametric models for python

Orgainzed Digital Intelligent Network (O.D.I.N)

Curated materials for different machine learning related summer schools

Materials for Graph Models and Graph Networks

A collection of commonly used datasets as benchmarks for density estimation in MaLe

Probability distributions in Clojure

R package for inference in Bayesian networks.

Code for paper: MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation

The homework assignments finished for the coursera specialization "Probabilistic Graphical Models"

Undergraduate graduation project (Entity Linking System in Web Tables with Multiple Linked Knowledge Bases) at SEU.

Implementation of the Paper "Entity Linking in Web Tables with Multiple Linked Knowledge Bases"

🌀 Stanford CS 228 - Probabilistic Graphical Models

An analysis and implementation of Conditional Random Fields, which are used when the input data is dependent on each other.

Discourse-Aware Sentiment Analysis

assignments and group case studies from PGDMLAI course by upGrad & IIITB