# aws-samples/aws-machine-learning-university-accelerated-cv

Machine Learning University: Accelerated Computer Vision Class

Repository: https://github.com/aws-samples/aws-machine-learning-university-accelerated-cv
Canonical: https://ross.abutalabs.com/products/aws-machine-learning-university-accelerated-cv
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: machine-learning, computer-vision, deep-learning, python, gluon, mxnet, gluoncv
Last push: 2024-10-12T23:50:58+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": 2226, "days_push": 690, "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 1613, forks 362 (observed 2026-08-28T04:05:11.218917+00:00)

## What it is
A free Machine Learning University course from AWS covering accelerated computer vision, with slides, Jupyter notebooks, datasets, and YouTube lecture videos. Topics span neural networks, CNNs, transfer learning, object detection (YOLO), and semantic segmentation using GluonCV/MXNet.

## Use cases
- learn computer vision from scratch
- self-study deep learning for image classification
- find course materials for teaching CNNs
- practice object detection with YOLO notebooks
- learn semantic segmentation basics
- get started with GluonCV and MXNet
- watch free university-level ML lectures

## When to choose
- you want a structured, free CV course with videos and hands-on notebooks
- you are a beginner to intermediate learner in deep learning for vision
- you want runnable notebooks on image classification, detection, and segmentation

## When to avoid
- you need production computer vision code or a maintained library
- you require PyTorch or TensorFlow examples instead of MXNet/GluonCV
- you need actively updated course content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, computer-vision, deep-learning
- domain: computer-vision, machine-learning, deep-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, course-material, gluoncv, mxnet, aws, youtube-lectures, image-classification, object-detection, semantic-segmentation

## Member repositories
- aws-samples/aws-machine-learning-university-accelerated-cv (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:11.218917+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 2026-08-30T03:51:05.506694+00:00, confidence not recorded.
  - readme: https://github.com/aws-samples/aws-machine-learning-university-accelerated-cv (fetched 2026-08-28T04:05:11.218917+00:00, sha 774462c47f47)
- Data as of 2026-08-30T08:39:29.467469+00:00.