probml/pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Comprehensive implementation of probabilistic ML algorithms across NumPy, JAX, TensorFlow, and PyTorch, with executable Jupyter notebooks organized by textbook chapter that reproduce all figures from Murphy's two-volume PML series. Uses a modular architecture with shared utility functions (probml-utils) and supports GPU/TPU execution via Colab, Lightning.ai, or GCP—enabling reproducible research across different computational backends without framework lock-in.
7,034 stars.
Stars
7,034
Forks
1,613
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 26, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/probml/pyprobml"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks....
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
astro-informatics/harmonic
Machine learning assisted marginal likelihood (Bayesian evidence) estimation for Bayesian model selection
cdslaborg/paramonte
ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
tensorflow/probability
Probabilistic reasoning and statistical analysis in TensorFlow