# geohot/ai-notebooks

Some ipython notebooks implementing AI algorithms

Repository: https://github.com/geohot/ai-notebooks
Canonical: https://ross.abutalabs.com/products/ai-notebooks
Language: Jupyter Notebook
License Family: other
Last push: 2025-05-15T07:10:27+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 21, release rhythm 35, longevity 100
- inputs: {"age_days": 2708, "days_push": 475, "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 1425, forks 218 (observed 2026-08-28T04:04:41.541864+00:00)

## What it is
A collection of Jupyter notebooks by George Hotz implementing simple AI and machine learning algorithms in Python 3 using TensorFlow 2, PyTorch, and Keras. It is intended as an educational reference designed for viewing on GitHub.

## Use cases
- learn how ML algorithms work from scratch
- study neural network implementations in pytorch and tensorflow
- find example notebook implementations of AI algorithms
- reference simple deep learning code while learning
- explore educational machine learning experiments

## When to choose
- you want readable, minimal implementations of AI algorithms for learning
- you prefer notebook-style tutorials over full libraries
- you want to compare implementations across TensorFlow, PyTorch, and Keras

## When to avoid
- you need production-ready, tested, or maintained ML code
- you require a licensed dependency for commercial use (no license is provided)
- you need a packaged library with an API rather than example notebooks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, artificial-intelligence, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, tensorflow, pytorch, keras, educational, algorithms

## Member repositories
- geohot/ai-notebooks (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:41.541864+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 2026-08-30T04:37:31.134680+00:00, confidence not recorded.
  - readme: https://github.com/geohot/ai-notebooks (fetched 2026-08-28T04:04:41.541864+00:00, sha 85c7d8df7ab3)
- Data as of 2026-08-30T08:39:29.467469+00:00.