# probml/pyprobml

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

Repository: https://github.com/probml/pyprobml
Canonical: https://ross.abutalabs.com/products/pyprobml
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: jupyter-notebooks, machine-learning, colab, pml, probabilistic-programming, jax, tensorflow, pytorch, pymc3, numpyro, flax, pyro, blackjax
Last push: 2026-02-26T17:43:52+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 69, release rhythm 8, longevity 100
- inputs: {"age_days": 3668, "days_push": 188, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 7132, forks 1629 (observed 2026-08-28T04:09:56.121776+00:00)

## What it is
A collection of Jupyter notebooks with Python code reproducing the figures from Kevin Murphy's 'Probabilistic Machine Learning' books (Introduction and Advanced Topics). The code uses numpy, scipy, sklearn, JAX, TensorFlow, and PyTorch, and is designed to run locally or in Google Colab.

## Use cases
- reproduce figures from the probabilistic machine learning book
- learn probabilistic machine learning with runnable code examples
- study JAX and probabilistic programming through notebooks
- find worked examples of Bayesian machine learning algorithms
- run machine learning textbook notebooks in Google Colab
- learn numpyro, blackjax, and flax through examples

## When to choose
- you are reading Kevin Murphy's PML books and want accompanying code
- you want educational notebooks on probabilistic ML and Bayesian methods
- you want examples across multiple frameworks like JAX, TensorFlow, and PyTorch

## When to avoid
- you need a production-ready machine learning library
- you need actively developed or supported software - the code is in maintenance mode
- you need a curated course rather than figure-reproduction notebooks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-visualization, developer-tools
- domain: machine-learning, education, data-science, tutorials
- platform: python, jvm-scripting, cross-platform, browser
- tags: jupyter-notebooks, probabilistic-machine-learning, jax, tensorflow, pytorch, kevin-murphy, textbook-code, colab, probabilistic-programming

## Member repositories
- probml/pyprobml (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:56.121776+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:40:03.410209+00:00, confidence not recorded.
  - readme: https://github.com/probml/pyprobml (fetched 2026-08-28T04:09:56.121776+00:00, sha a823a618fe92)
- Data as of 2026-08-30T08:39:29.467469+00:00.