The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

A game theoretic approach to explain the output of any machine learning model.

Solve puzzles. Learn CUDA.

Playing Pokemon Red with Reinforcement Learning

"Probabilistic Machine Learning" - a book series by Kevin Murphy

Simple UI for LLM Model Finetuning

PyMC educational resources

Source code accompanying O'Reilly book: Machine Learning Design Patterns

Instant-ngp in pytorch+cuda trained with pytorch-lightning (high quality with high speed, with only few lines of legible code)

Named tensors with first-class dimensions for PyTorch

Implement the-state-of-the-art meta-heuristic algorithms using python (numpy)

Causal Graphical Models in Python

A Python package for automatic and optimized resource scheduling