hsf-training-ml-webpage
Introduction to machine learning
Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (16.2%) to scientific vocabulary
Keywords
Repository
Introduction to machine learning
Basic Info
- Host: GitHub
- Owner: hsf-training
- License: other
- Language: HTML
- Default Branch: gh-pages
- Homepage: https://hsf-training.github.io/hsf-training-ml-webpage
- Size: 5.64 MB
Statistics
- Stars: 12
- Watchers: 5
- Forks: 13
- Open Issues: 3
- Releases: 0
Topics
Metadata Files
README.md
Machine Learning for Particle Physics
Note Click here for the training website!
This tutorial explores Machine Learning using scikit-learn and PyTorch for applications in high energy physics.
Extended from a version developed by Luke Polson for the 2020 USATLAS Computing Bootcamp.
📅 Past events and videos
Emoji key: 🎥 (full video recordings availabile), ⛏️ (hackathon)
🤗 Contributing
We welcome all contributions to improve the lesson! Maintainers will do their best to help you if you have any questions, concerns, or experience any difficulties along the way.
If you make non-trivial changes (i.e., more than fixing a simple typo), you are eligible to be added to the HSF Training Community page, as well as to the list of contributors below.
We'd like to ask you to familiarize yourself with our Contribution Guide and have a look at the more detailed guidelines on proper formatting, ways to render the lesson locally, and even how to write new episodes.
Quick summary of how to get a local preview: Install jekyll and then run
bundle install
bundle update
bundle exec jekyll serve
Unless we change framework versions, only the last command needs to be typed after the first time.
Before committing anything, we also ask you to install the pre-commit hooks of this repository:
bash
pip3 install pre-commit
pre-commit install
Please see the current list of issues for ideas for contributing to this repository. For making your contribution, we use the GitHub flow, which is nicely explained in the chapter Contributing to a Project in Pro Git by Scott Chacon. Look for the tag ![goodfirstissue], which marks particularly simple issues to get you started.
![![goodfirstissue]](https://img.shields.io/badge/-good%20first%20issue-gold.svg){kind=link}
Authors
- Meirin Oan Evans
💖 Authors
Thanks goes to these wonderful people (emoji key) who contributed to the content of the lesson:
Even more people contributed to the framework, but they are too many to list! Instead, all regular contributors are listed on our HSF Training Community page.
Owner
- Name: HEP Software Foundation Training Material
- Login: hsf-training
- Kind: organization
- Email: hsf-coordination@googlegroups.com
- Website: https://hepsoftwarefoundation.org/training/
- Twitter: HsfTraining
- Repositories: 28
- Profile: https://github.com/hsf-training
Training and educational material for the high energy physics community.
Citation (CITATION)
FIXME: describe how to cite this lesson.
GitHub Events
Total
- Issue comment event: 1
- Push event: 2
- Create event: 1
Last Year
- Issue comment event: 1
- Push event: 2
- Create event: 1
Dependencies
- github-pages >= 0 development
- actions/cache v3 composite
- actions/checkout master composite
- actions/setup-python v4 composite
- actions/setup-ruby main composite
- r-lib/actions/setup-r master composite
- actions/cache v3 composite
- actions/checkout master composite
- actions/setup-python v4 composite
- actions/setup-ruby main composite
- r-lib/actions/setup-r master composite