Resources and materials for a DataScience / AI training school for the medical domain, conducted as part of the Come2Data (https://come2data.de/) project.
You can read the training content under this URL:
https://scads.github.io/ai4medicine-2025
The following instructions are just for contributors and trainers.
- We maintain a common conda environment based on conda-ai4med-env.yml
- If you want to make use of specific Python libraries for your session, add them to the conda env file
- Provide prepared slides as pptx in slides
- Put a PDF version of the slides to the session folder and link it in the according readme.md
If you'd like to develop and/or build the Jupyter book locally, you should:
- Clone this repository
- Run
pip install -r requirements.txt(it is recommended you do this within a virtual environment) - (Optional) Edit the books source files.
- Run
jupyter-book clean .to remove any existing builds - Run
jupyter-book build .
A fully-rendered HTML version of the book will be built in /_build/html/. Make sure to never upload this folder to github.
Please see the Jupyter Book documentation to discover options for deploying a book online using services such as GitHub, GitLab, or Netlify.
For GitHub and GitLab deployment specifically, the cookiecutter-jupyter-book includes templates for, and information about, optional continuous integration (CI) workflow files to help easily and automatically deploy books online with GitHub or GitLab. For example, if you chose github for the include_ci cookiecutter option, your book template was created with a GitHub actions workflow file that, once pushed to GitHub, automatically renders and pushes your book to the gh-pages branch of your repo and hosts it on GitHub Pages when a push or pull request is made to the main branch.
We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.
This project is created using the excellent open source Jupyter Book project and the executablebooks/cookiecutter-jupyter-book template.
We acknowledge the financial support by the Federal Ministry of Education and Research of Germany and by Sächsische Staatsministerium für Wissenschaft, Kultur und Tourismus in the programmes „Stärkung der Datenkompetenzen des wissenschaftlichen Nachwuchses“, project identification number: 16DKZ2044; and Center of Excellence for AI-research „Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig“, project identification number: ScaDS.AI.