# romeokienzler/TensorFlow

Project containig related material for my TensorFlow articles

Repository: https://github.com/romeokienzler/TensorFlow
Canonical: https://ross.abutalabs.com/products/romeokienzler-tensorflow
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2024-06-17T12:03:01+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": 2667, "days_push": 807, "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 2605, forks 8471 (observed 2026-08-28T04:07:04.023248+00:00)

## What it is
A collection of Jupyter notebooks and supporting material accompanying the author's TensorFlow articles, focused on TensorFlow 2.x features like eager execution and the Keras API. It serves as a hands-on tutorial resource with 'watch me coding' videos and exercises.

## Use cases
- learn tensorflow 2.x features
- understand eager execution in tensorflow
- learn keras api integration with tensorflow
- hands-on deep learning exercises with jupyter notebooks
- follow along with tensorflow tutorial videos
- practice distributed training concepts in tensorflow

## When to choose
- you are learning TensorFlow 2.x from articles and want companion notebooks
- you prefer video-plus-coding-exercise style tutorials
- you want to understand the Keras API inside TensorFlow

## When to avoid
- you need production TensorFlow code or a maintained library
- you want a complete structured course rather than article companion material
- you need up-to-date coverage of the latest TensorFlow releases

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials
- platform: python, cross-platform
- tags: tensorflow, jupyter-notebooks, keras, educational-material, deep-learning-tutorials

## Member repositories
- romeokienzler/TensorFlow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:04.023248+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 2026-08-30T02:21:03.083651+00:00, confidence not recorded.
  - readme: https://github.com/romeokienzler/TensorFlow (fetched 2026-08-28T04:07:04.023248+00:00, sha df616bc14655)
- Data as of 2026-08-30T08:39:29.467469+00:00.