# aymericdamien/TensorFlow-Examples

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Repository: https://github.com/aymericdamien/TensorFlow-Examples
Canonical: https://ross.abutalabs.com/products/tensorflow-examples
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: tensorflow, tutorial, examples, deep-learning, python, machine-learning
Last push: 2024-07-26T19:46:13+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": 3948, "days_push": 768, "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 43735, forks 14657 (observed 2026-08-28T04:12:09.098399+00:00)

## What it is
A collection of TensorFlow tutorials and example notebooks designed for beginners, covering both TF v1 and v2 APIs. It includes notebooks and source code with explanations spanning basic operations, regression models, and neural networks.

## Use cases
- learn tensorflow from scratch
- find example code for building neural networks
- understand difference between tensorflow v1 and v2
- get started with deep learning in python
- see how to implement linear and logistic regression in tensorflow
- study mnist classification examples

## When to choose
- you are a beginner wanting clear, concise tensorflow examples
- you prefer learning through runnable notebooks with explanations
- you need both legacy TF1 and modern TF2 code references

## When to avoid
- you need production-ready or maintained deep learning code
- you want tutorials for PyTorch or other frameworks
- you need advanced or state-of-the-art model implementations

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: tensorflow, jupyter-notebooks, beginner-friendly, neural-networks, educational

## Member repositories
- aymericdamien/TensorFlow-Examples (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:09.098399+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 2026-08-29T16:22:21.112545+00:00, confidence not recorded.
  - readme: https://github.com/aymericdamien/TensorFlow-Examples (fetched 2026-08-28T04:12:09.098399+00:00, sha 2755c4edb5f1)
- Data as of 2026-08-30T08:39:29.467469+00:00.