# sjchoi86/Tensorflow-101

TensorFlow Tutorials

Repository: https://github.com/sjchoi86/Tensorflow-101
Canonical: https://ross.abutalabs.com/products/tensorflow-101
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: tensorflow-tutorials, convolutional-neural-networks, recurrent-neural-networks
Last push: 2020-03-23T06:50:22+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": 3768, "days_push": 2354, "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 2591, forks 729 (observed 2026-08-28T04:07:02.946664+00:00)

## What it is
A collection of TensorFlow tutorials written as Jupyter Notebooks, aimed at TensorFlow and deep learning beginners. It covers basics through CNNs, RNNs, and fine-tuning pre-trained models like VGG.

## Use cases
- learn tensorflow from scratch
- understand convolutional neural networks with mnist
- learn how to build recurrent neural networks
- fine-tune a pretrained vgg model on custom data
- get example notebooks for linear and logistic regression
- find a recipe book of deep learning examples

## When to choose
- you are a beginner wanting kindly explained TensorFlow notebooks
- you want runnable examples covering MLP, CNN, and RNN basics
- you want to learn fine-tuning pretrained models like VGG

## When to avoid
- you need tutorials for TensorFlow 2.x or current APIs
- you need production-grade code rather than educational notebooks
- you need actively maintained content with recent updates

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, data-science
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, cross-platform
- tags: tensorflow, jupyter-notebook, cnn, rnn, mnist, vgg, educational, natural-language-processing

## Member repositories
- sjchoi86/Tensorflow-101 (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:02.946664+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 2026-08-30T02:22:19.334040+00:00, confidence not recorded.
  - readme: https://github.com/sjchoi86/Tensorflow-101 (fetched 2026-08-28T04:07:02.946664+00:00, sha 56c888c36f55)
- Data as of 2026-08-30T08:39:29.467469+00:00.