deep-learning-physics and MachineLearning_Physics
These two tools are complements, as the textbook material and course facilitate a comprehensive learning experience in "Machine Learning in Physics."
About deep-learning-physics
DeepLearningForPhysicsResearchBook/deep-learning-physics
This project contains additional material for the textbook Deep Learning for Physics Research by Martin Erdmann, Jonas Glombitza, Gregor Kasieczka, and Uwe Klemradt.
This project provides practical exercises for individuals studying deep learning applications in physics. It offers hands-on problems that complement the 'Deep Learning for Physics Research' textbook, allowing students and researchers to apply theoretical knowledge to real physics data and simulations. The material is designed for physics students, researchers, and academics looking to enhance their deep learning skills relevant to their field.
About MachineLearning_Physics
sraeisi/MachineLearning_Physics
This is to facilitate the “Machine Learning in Physics” course that I am teaching at Sharif University of Technology for winter-19 semester. For more information, see the course page at
This repository provides comprehensive course materials for learning how machine learning techniques are applied in physics. It includes lecture notes, interactive coding notebooks, and video lectures that cover topics from basic machine learning concepts to neural networks. Students and researchers in physics can use these resources to understand and implement machine learning solutions for their scientific problems.
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