Open-source framework for the research and development of foundation models.

pymc-learn: Practical probabilistic machine learning in Python

A Python package for causal inference in quasi-experimental settings

Bayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.

Multi-purpose data analysis framework based on Bayesian networks and Causal models

code and demo for hierarchical stacking paper

A Grammar of Graphics for Python

Code for the Book Causal Inference in Python

Contains examples to illustrate the concepts of causal inference

Support files for the O'Reilly book "Behavioral Data Analysis with R and Python" by Florent Buisson

Материалы по курсу анализу данных

Mastering Transformers, published by Packt

Rethinking Knowledge Graph Propagation for Zero-Shot Learning, in CVPR 2019

Python code for "Probabilistic Machine learning" book by Kevin Murphy

Parallel Loops in Python

Open Machine Learning Course

Uncertainty quantification with PyTorch

All things Monte Carlo, written in JAX.

Graph Optimiser for Learning and Evolution of Models

A Python library that implements scoring utilities, analysis strategies, and visualization methods which can serve uplift modeling use-cases.

Python implementation of 'Scalable Recommendation with Hierarchical Poisson Factorization'.

Bayesian active learning library for research and industrial usecases.

A collection of visual guides to help applied scientists learn causal inference.

A Python package for modular causal inference analysis and model evaluations

IMP module for Hamiltonian Monte Carlo

Statistical Rethinking (2nd ed.) with NumPyro

Statistical Rethinking Course for Jan-Mar 2023