iml-wg/HEP-ML-Resources

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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