# ageron/tf2_course

Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

Repository: https://github.com/ageron/tf2_course
Canonical: https://ross.abutalabs.com/products/tf2_course
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2023-05-23T01:08:50+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2797, "days_push": 1199, "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 1909, forks 532 (observed 2026-08-28T04:05:53.004842+00:00)

## What it is
A collection of Jupyter notebooks with exercises and solutions accompanying Aurélien Géron's 'Deep Learning with TensorFlow 2 and Keras' training course. It covers neural networks and deep learning topics using TensorFlow 2 and Keras.

## Use cases
- learn deep learning with tensorflow 2 and keras
- practice neural network exercises with solutions
- follow a hands-on deep learning course in notebooks
- run tensorflow tutorials in colab or binder
- study keras neural nets with worked examples

## When to choose
- you want structured course material with exercises and solutions for TensorFlow 2 and Keras
- you prefer learning through runnable Jupyter notebooks in Colab, Binder, or locally
- you are training or self-studying deep learning fundamentals

## When to avoid
- you need production code or a maintained library rather than educational notebooks
- you want the latest TensorFlow APIs, as the material was last updated in 2023
- you are looking for the Hands-on Machine Learning book code, which lives in ageron/handson-ml2

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: deep-learning, machine-learning, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, tensorflow, keras, course-material, exercises, gpu

## Member repositories
- ageron/tf2_course (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.004842+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 2026-08-30T03:11:06.065758+00:00, confidence not recorded.
  - readme: https://github.com/ageron/tf2_course (fetched 2026-08-28T04:05:53.004842+00:00, sha fc82d47b123d)
- Data as of 2026-08-30T08:39:29.467469+00:00.