# serengil/tensorflow-101

TensorFlow 101: Introduction to Deep Learning

Repository: https://github.com/serengil/tensorflow-101
Canonical: https://ross.abutalabs.com/products/serengil-tensorflow-101
Homepage: https://www.youtube.com/watch?v=YjYIMs5ZOfc&list=PLsS_1RYmYQQGxpKV44jsxXNgjEpRoW61w&index=2
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: tensorflow, python, neural-networks, deep-learning, machine-learning, face-recognition, facial-expression-recognition, style-transfer, autoencoders, transfer-learning, convolutional-neural-networks, age-prediction, gender-prediction, celebrity-recognition, automl, vgg-face, facenet, openface, deepface, emotion-analysis
Last push: 2025-07-09T12:44:43+00:00

## Health v2 (maintenance only)
Score: 46/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 30, release rhythm 35, longevity 100
- inputs: {"age_days": 3346, "days_push": 420, "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 1099, forks 627 (observed 2026-08-28T04:03:35.026139+00:00)

## What it is
A collection of Jupyter notebooks and Python scripts teaching deep learning with TensorFlow and Keras through hands-on projects. It covers face recognition, facial expression recognition, style transfer, autoencoders, and transfer learning with step-by-step tutorials and pre-trained model weights.

## Use cases
- learn deep learning with tensorflow from scratch
- build a facial expression recognition model
- implement face recognition with vgg-face or facenet
- understand transfer learning and autoencoders with code examples
- follow step-by-step cnn tutorials with documentation
- run real-time emotion analysis on video

## When to choose
- you want tutorial-style deep learning code with explanations
- you are learning TensorFlow/Keras through computer vision projects
- you need reference implementations of face recognition models

## When to avoid
- you need a production-ready face recognition library (use deepface instead)
- you want a maintained framework rather than educational notebooks
- you need frameworks other than TensorFlow/Keras

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, tutorials, education
- platform: python
- tags: tensorflow, keras, jupyter-notebooks, face-recognition, facial-expression-recognition, style-transfer, autoencoders, transfer-learning, convolutional-neural-networks, vgg-face, facenet, step-by-step-tutorial

## Member repositories
- serengil/tensorflow-101 (main) score 46

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:35.026139+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 2026-08-30T06:46:04.935687+00:00, confidence not recorded.
  - readme: https://github.com/serengil/tensorflow-101 (fetched 2026-08-28T04:03:35.026139+00:00, sha a9f325e57790)
  - homepage: https://www.youtube.com/watch?v=YjYIMs5ZOfc&list=PLsS_1RYmYQQGxpKV44jsxXNgjEpRoW61w&index=2 (fetched 2026-08-29T12:49:32.924598+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.