HEPML-LivingReview and HEP-ML-Resources
These are complementary resources where the Living Review provides in-depth technical analysis of ML methods applied to specific HEP problems, while HEP-ML-Resources serves as a curated index of external learning materials and tools to support that learning journey.
About HEPML-LivingReview
iml-wg/HEPML-LivingReview
Living Review of Machine Learning for Particle Physics
This document helps particle physicists stay current with the rapidly evolving field of machine learning applications in high-energy physics. It provides a frequently updated, curated list of research papers and reviews, organized by topic. This resource is for researchers, experimentalists, and theorists in particle physics looking to apply or develop modern machine learning techniques in their analyses.
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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