# tensorflow/workshops

A few exercises for use at events.

Repository: https://github.com/tensorflow/workshops
Canonical: https://ross.abutalabs.com/products/tensorflow-workshops
Homepage: https://tensorflow.org
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2021-04-27T18:31:29+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3303, "days_push": 1954, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1433, forks 664 (observed 2026-08-28T04:04:43.252993+00:00)

## What it is
A collection of Jupyter Notebook exercises from the TensorFlow team intended for use at workshops, events, and training sessions. It provides hands-on exercises for learning TensorFlow and machine learning concepts rather than production software.

## Use cases
- learn tensorflow through hands-on exercises
- find workshop materials for a machine learning bootcamp
- practice deep learning with jupyter notebooks
- prepare teaching materials for an ML event
- get started with tensorflow as a beginner
- run a tensorflow training session for a team

## When to choose
- you need ready-made notebook exercises for teaching TensorFlow at an event or class
- you want hands-on practice with TensorFlow rather than reading docs
- you are organizing a workshop and need structured lab material

## When to avoid
- you need the TensorFlow library itself or production ML tooling
- you need actively maintained tutorials reflecting the latest TensorFlow APIs, since the repo has not seen releases since 2021
- you prefer structured courses or documentation over self-guided notebooks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: machine-learning, deep-learning, education, tutorials
- platform: python, cross-platform, cli
- tags: jupyter-notebooks, workshops, exercises, tensorflow, hands-on-learning, event-materials

## Member repositories
- tensorflow/workshops (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:43.252993+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 2026-08-30T04:36:58.966339+00:00, confidence not recorded.
  - homepage: https://tensorflow.org (fetched 2026-08-29T11:48:29.815021+00:00, sha ab510dcb6040)
  - site_page: https://www.tensorflow.org/install (fetched 2026-08-29T11:48:29.824233+00:00, sha 584a762da891)
  - site_page: https://www.tensorflow.org/tfx/api_docs (fetched 2026-08-29T11:48:29.851813+00:00, sha 6977825696fe)
  - site_page: https://www.tensorflow.org/about (fetched 2026-08-29T11:48:29.855875+00:00, sha 817250744d91)
  - site_page: https://www.tensorflow.org/about/case-studies (fetched 2026-08-29T11:48:29.858217+00:00, sha e6701029eec0)
  - site_page: https://www.tensorflow.org/about/bib (fetched 2026-08-29T11:48:29.863536+00:00, sha 5ca8943386b9)
  - site_page: https://www.tensorflow.org/community/contribute (fetched 2026-08-29T11:48:29.853751+00:00, sha 77d394fa76e7)
  - site_page: https://blog.tensorflow.org/2024/02/graph-neural-networks-in-tensorflow.html (fetched 2026-08-29T11:48:29.860680+00:00, sha fd6c6556fa89)
- Data as of 2026-08-30T08:39:29.467469+00:00.