# bayesgroup/deepbayes-2018

Seminars DeepBayes Summer School 2018

Repository: https://github.com/bayesgroup/deepbayes-2018
Canonical: https://ross.abutalabs.com/products/deepbayes-2018
Language: Jupyter Notebook
License Family: other
Topics: deep-learning, bayesian, variational-inference
Last push: 2019-08-24T16:04:53+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": 2930, "days_push": 2566, "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 1045, forks 268 (observed 2026-08-28T04:03:21.464172+00:00)

## What it is
A collection of Jupyter Notebook seminars from the DeepBayes Summer School 2018, run by the BayesGroup. It covers deep learning with a focus on Bayesian methods and variational inference, runnable interactively via Binder.

## Use cases
- learn bayesian deep learning from notebooks
- study variational inference with hands-on examples
- find deep learning summer school course materials
- practice probabilistic deep learning in jupyter
- self-study materials on bayesian neural networks
- teach a course on variational methods in deep learning

## When to choose
- you want free, notebook-based tutorials on Bayesian deep learning and variational inference
- you prefer runnable code you can explore in Binder or locally
- you are an instructor looking for ready-made seminar exercises

## When to avoid
- you need a maintained software library or production tool
- you want up-to-date materials reflecting current deep learning practice (content is from 2018)
- you need a licensed, redistributable codebase (no license is specified)

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, data-science
- domain: deep-learning, machine-learning, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, bayesian-methods, variational-inference, summer-school, seminars, education

## Member repositories
- bayesgroup/deepbayes-2018 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:21.464172+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 2026-08-30T07:02:03.455127+00:00, confidence not recorded.
  - readme: https://github.com/bayesgroup/deepbayes-2018 (fetched 2026-08-28T04:03:21.464172+00:00, sha cd785213f7e5)
- Data as of 2026-08-30T08:39:29.467469+00:00.