# ctgk/PRML

PRML algorithms implemented in Python

Repository: https://github.com/ctgk/PRML
Canonical: https://ross.abutalabs.com/products/prml
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: jupyter, prml, notebook, python
Last push: 2025-04-05T02:01:37+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 14, release rhythm 35, longevity 100
- inputs: {"age_days": 3496, "days_push": 516, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11723, forks 3207 (observed 2026-08-28T04:10:49.586463+00:00)

## What it is
A collection of Python implementations of algorithms from Bishop's textbook 'Pattern Recognition and Machine Learning', organized as Jupyter notebooks per chapter. It serves as an educational companion for studying classical machine learning methods.

## Use cases
- learn machine learning algorithms from Bishop's PRML book
- study pattern recognition with runnable Python code
- understand Bayesian machine learning implementations
- explore neural networks, kernel methods, and Gaussian processes in notebooks
- reference implementations of classic ML algorithms in numpy

## When to choose
- you are studying the PRML textbook and want code to accompany each chapter
- you want clean numpy/scipy implementations of classic ML algorithms
- you prefer learning through interactive Jupyter notebooks

## When to avoid
- you need production-ready, optimized ML libraries like scikit-learn or PyTorch
- you want modern deep learning techniques not covered in the book
- you need GPU-accelerated or scalable training

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-visualization, math
- domain: machine-learning, education, tutorials
- platform: python, cross-platform
- tags: prml, bishop, jupyter-notebooks, bayesian-machine-learning, educational, algorithms

## Member repositories
- ctgk/PRML (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:49.586463+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 2026-08-29T17:15:22.515192+00:00, confidence not recorded.
  - readme: https://github.com/ctgk/PRML (fetched 2026-08-28T04:10:49.586463+00:00, sha e2516e8073fa)
- Data as of 2026-08-30T08:39:29.467469+00:00.