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.
About HEP-ML-Resources
iml-wg/HEP-ML-Resources
Listing of useful learning resources for machine learning applications in high energy physics (HEPML)
Organized collection of curated lectures, tutorials, datasets, and papers spanning foundational ML concepts (GANs, deep learning, boosted decision trees) through domain-specific applications in particle detection and reconstruction. Structures resources by learning level and format—from introductory seminars and hands-on Jupyter notebooks to archived summer school curricula—enabling practitioners to navigate ML adoption across the HEP ecosystem including PyTorch, Keras, scikit-learn, and TMVA frameworks.
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