dair-ai/ML-Notebooks resource
:fire: Machine Learning Notebooks observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1621
- days_rel: n/a
- days_push: 876
- n_releases_24m: 0
Adoption not part of the score
3436 stars · 538 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A collection of minimal, reusable Jupyter notebooks covering machine learning and deep learning tasks, built primarily with PyTorch. It serves as an educational resource with runnable examples in Colab or GitHub Codespaces.
Use cases
- learn pytorch with hands-on notebooks
- machine learning tutorials for beginners
- implement linear regression from scratch
- understand computational graphs
- learn explainable AI with counterfactual explanations
- find reusable ML notebook examples
When to choose
- you want minimal, runnable PyTorch examples to learn from
- you prefer notebook-based, hands-on learning
- you need educational material for ML courses or self-study
When to avoid
- you need production-ready ML code or pipelines
- you want a maintained library or framework with an API
- you need coverage of advanced or specialized ML topics
Facets
learning-resource · maturity maintenance
machine-learning deep-learning developer-tools machine-learning deep-learning education tutorials python cross-platform jupyter-notebooks pytorch educational colab hands-on-tutorials web
1 source
- readme: https://github.com/dair-ai/ML-Notebooks · fetched 2026-08-28 · acbd40b68dce
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| dair-ai/ML-Notebooks | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="dair-ai/ML-Notebooks")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem