# nlintz/TensorFlow-Tutorials

Simple tutorials using Google's TensorFlow Framework

Repository: https://github.com/nlintz/TensorFlow-Tutorials
Canonical: https://ross.abutalabs.com/products/nlintz-tensorflow-tutorials
Language: Jupyter Notebook
License Family: other
Last push: 2023-08-20T07:32:57+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": 3949, "days_push": 1109, "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 6028, forks 1479 (observed 2026-08-28T04:09:34.387682+00:00)

## What it is
A collection of simple tutorial scripts introducing deep learning with Google's TensorFlow, ported from Theano tutorials. It covers topics from linear regression through CNNs, LSTMs, autoencoders, and GANs.

## Use cases
- learn deep learning basics with tensorflow
- tutorial on building a convolutional neural network
- example of an lstm in tensorflow
- understand autoencoders with code
- get started with tensorflow 1.0
- learn word2vec implementation
- example of a gan in tensorflow

## When to choose
- you want short, readable example scripts for classic deep learning models
- you are learning TensorFlow 1.x fundamentals
- you want ports of Theano tutorials to TensorFlow

## When to avoid
- you use TensorFlow 2.x or Keras, since these target TensorFlow 1.0 alpha
- you need maintained, up-to-date tutorials
- you need production-ready code or a license for reuse

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: deep-learning, machine-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: tensorflow, jupyter-notebook, neural-networks, tutorials, educational

## Member repositories
- nlintz/TensorFlow-Tutorials (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:34.387682+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 2026-08-29T17:49:14.209897+00:00, confidence not recorded.
  - readme: https://github.com/nlintz/TensorFlow-Tutorials (fetched 2026-08-28T04:09:34.387682+00:00, sha 0111db041831)
- Data as of 2026-08-30T08:39:29.467469+00:00.