Introduction to deep learning for particle physicists

Material for introducing deep neural networks to students with a particle physics background.

Usage

Building the book

If you'd like to develop and/or build the Introduction to deep learning for particle physicists book, you should:

1. Clone this repository
2. Run pip install -r requirements.txt (it is recommended you do this within a virtual environment)
3. (Optional) Edit the books source files located in the deep-learning-intro-for-hep/ directory
4. Run jupyter-book clean deep-learning-intro-for-hep/ to remove any existing builds
5. Run jupyter-book build deep-learning-intro-for-hep/

A fully-rendered HTML version of the book will be built in deep-learning-intro-for-hep/_build/html/.

Hosting the book

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.

Contributors

We welcome and recognize all contributions. You can see a list of current contributors in the contributors tab.

Credits

This project is created using the excellent open source Jupyter Book project and the executablebooks/cookiecutter-jupyter-book template.