# phlippe/uvadlc_notebooks

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

Repository: https://github.com/phlippe/uvadlc_notebooks
Canonical: https://ross.abutalabs.com/products/uvadlc_notebooks
Homepage: https://uvadlc-notebooks.readthedocs.io/en/latest/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: deep-learning, tutorials, uvadlc, pytorch, pytorch-lightning, tutorial, flax, jax, optax
Last push: 2026-06-01T10:24:56+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 85, release rhythm 35, longevity 100
- inputs: {"age_days": 2230, "days_push": 93, "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 3181, forks 682 (observed 2026-08-28T04:07:47.840080+00:00)

## What it is
A collection of Jupyter notebook tutorials for the University of Amsterdam's MSc AI Deep Learning course, covering topics like optimization, transformers, and graph neural networks with implementations in PyTorch, PyTorch Lightning, and JAX/Flax. The notebooks are runnable on CPU laptops and are also published as official PyTorch Lightning tutorials.

## Use cases
- learn deep learning concepts through hands-on notebook implementations
- understand how transformers work in PyTorch
- learn PyTorch Lightning with guided tutorials
- compare PyTorch and JAX/Flax implementations side by side
- study graph neural networks with runnable code
- prepare for a university deep learning course or exam

## When to choose
- you want structured, course-quality tutorials with runnable code
- you prefer learning deep learning theory via implementations
- you want to learn PyTorch Lightning or JAX/Flax from worked examples
- you need tutorials that run on a CPU-only laptop

## When to avoid
- you need production-ready deep learning code or a library
- you want a comprehensive reference rather than guided tutorials
- you need topics outside the course syllabus such as reinforcement learning or MLOps

## Facets
- artifact type: learning-resource
- maturity: active
- function: deep-learning, machine-learning
- domain: deep-learning, tutorials, education
- platform: python
- tags: jupyter-notebooks, pytorch-lightning, flax, transformers, graph-neural-networks, university-course, jax, jupyter

## Member repositories
- phlippe/uvadlc_notebooks (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:47.840080+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 2026-08-30T07:25:04.726709+00:00, confidence not recorded.
  - readme: https://github.com/phlippe/uvadlc_notebooks (fetched 2026-08-28T04:07:47.840080+00:00, sha e4f8c4d80978)
- Data as of 2026-08-30T08:39:29.467469+00:00.