Open source data science tutorials for MFlux.ai
Contributions are welcome! You can either improve existing tutorials or create new ones.
Each tutorial resides in its own folder. The folder name is the "slug" that defines the last part of the URL where the tutorial will be published on the MFlux website. The folder name should consist of only ASCII letters, numbers, hyphen and underscore characters. Tutorial folders contain content.md (the tutorial content in markdown format) and metadata.json (some more information about the tutorial). Here is an example of a valid metadata.json file:
{
"title": "Document classification",
"short_description": "In this tutorial, we will create a simple classifier model that can input video metadata and output a category prediction.",
"tags": ["deep learning", "keras", "classification"],
"rank": 999,
"is_listed": true,
"cover_image_filename": "cover.png"
}Tutorials should describe a useful case, ideally with some real data instead of toy data. It should be relatively easy to follow the tutorial.
Cover images should be 374x260 or 748x520 and the format should be JPG or PNG