# fastai/numerical-linear-algebra

Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

Repository: https://github.com/fastai/numerical-linear-algebra
Canonical: https://ross.abutalabs.com/products/numerical-linear-algebra
Homepage: http://www.fast.ai/2017/07/17/num-lin-alg/
Language: Jupyter Notebook
License Family: other
Topics: numpy, python, linear-algebra, data-science, machine-learning, algorithms, deep-learning
Last push: 2024-04-16T14:44:18+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3396, "days_push": 869, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10965, forks 2678 (observed 2026-08-28T04:10:44.879376+00:00)

## What it is
A free online textbook of Jupyter notebooks for the fast.ai Computational Linear Algebra course, taught at the University of San Francisco. It covers numerical matrix computations with acceptable speed and accuracy using Python, NumPy, Scikit-Learn, Numba, and PyTorch, with accompanying YouTube lecture videos.

## Use cases
- learn computational linear algebra with code
- understand matrix decompositions like SVD and QR
- study numerical linear algebra for data science
- find a free linear algebra course with Jupyter notebooks
- learn how matrix computations achieve speed and accuracy
- prepare for deep learning by learning linear algebra
- follow along with fast.ai lecture videos using notebooks

## When to choose
- you want a code-first, top-down introduction to numerical linear algebra
- you learn best from executable Jupyter notebooks paired with video lectures
- you are a data scientist or ML practitioner needing practical matrix computation knowledge

## When to avoid
- you need a rigorous proof-based linear algebra textbook
- you need a maintained library or software tool rather than course material
- you require a formally licensed or regularly updated resource

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: math, data-science, machine-learning
- domain: mathematics, data-science, machine-learning, tutorials
- platform: python, cross-platform
- tags: linear-algebra, jupyter-notebooks, numerical-computation, free-course, numpy, matrix-computations

## Member repositories
- fastai/numerical-linear-algebra (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.879376+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 2026-08-29T17:17:16.971640+00:00, confidence not recorded.
  - readme: https://github.com/fastai/numerical-linear-algebra (fetched 2026-08-28T04:10:44.879376+00:00, sha 4918b0fcd575)
  - homepage: http://www.fast.ai/2017/07/17/num-lin-alg/ (fetched 2026-08-29T08:15:55.957597+00:00, sha bd67b5a00981)
- Data as of 2026-08-30T08:39:29.467469+00:00.