Popular GitHub Repositories for Bayesian Methods

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

AI orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

Machine learning, in numpy

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

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

Bayesian Modeling and Probabilistic Programming in Python

? LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

Deep universal probabilistic programming with Python and PyTorch

Automated Machine Learning with scikit-learn

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

A curated list of awesome self-supervised methods

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

A collection of important graph embedding, classification and representation learning papers with implementations.

Probabilistic reasoning and statistical analysis in TensorFlow

Text recognition (optical character recognition) with deep learning methods, ICCV 2019

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

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

Learning in infinite dimension with neural operators.

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence