hep_ml and HEP-ML-Resources
The first is a practical ML library implementing algorithms for HEP data analysis, while the second is a curated directory of educational materials and resources—they are complements that serve different stages of learning and application in HEP-ML work.
About hep_ml
arogozhnikov/hep_ml
Machine Learning for High Energy Physics.
This project offers specialized machine learning tools to help high energy physicists analyze experimental data. It takes raw event data from particle collisions and applies classification and reweighting techniques to help identify significant patterns and remove unwanted biases. Particle physicists and researchers working with large datasets from accelerators would use this to improve their data analysis workflows.
About HEP-ML-Resources
iml-wg/HEP-ML-Resources
Listing of useful learning resources for machine learning applications in high energy physics (HEPML)
This project helps high-energy physicists navigate the rapidly growing field of machine learning applications in their domain. It provides a curated list of educational materials like lectures, seminars, tutorials, and schools, along with links to relevant software, datasets, and research papers. Anyone involved in high energy physics research or education who wants to learn about or apply machine learning will find this resource valuable.
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