# kuleshov/teaching-material

Teaching materials for the machine learning and deep learning classes at Stanford and Cornell

Repository: https://github.com/kuleshov/teaching-material
Canonical: https://ross.abutalabs.com/products/teaching-material
Language: Jupyter Notebook
License Family: other
Last push: 2020-09-03T19:30:36+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3891, "days_push": 2190, "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 1158, forks 1374 (observed 2026-08-28T04:03:48.479216+00:00)

## What it is
A collection of Jupyter notebook tutorials prepared as preparatory material for machine learning and deep learning courses at Stanford and Cornell. It primarily covers Python and NumPy fundamentals needed to get started with those classes.

## Use cases
- learn python and numpy for machine learning
- prepare for a stanford or cornell ml course
- find a python numpy tutorial notebook
- review python basics before a deep learning class
- teach an intro machine learning course with ready-made material

## When to choose
- you need a concise Python/NumPy refresher before starting an ML course
- you are an instructor looking for ready-made preparatory course material
- you prefer learning via runnable Jupyter notebooks

## When to avoid
- you need comprehensive machine learning course content beyond Python/NumPy basics
- you want actively maintained or licensed educational material
- you need tutorials on advanced deep learning topics

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp, data-science
- domain: machine-learning, deep-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebook, numpy, python-tutorial, course-material, stanford, cornell

## Member repositories
- kuleshov/teaching-material (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:48.479216+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 2026-08-30T06:31:57.613277+00:00, confidence not recorded.
  - readme: https://github.com/kuleshov/teaching-material (fetched 2026-08-28T04:03:48.479216+00:00, sha ffba64a54868)
- Data as of 2026-08-30T08:39:29.467469+00:00.