FlorianMarquardt/machine-learning-for-physicists
Code for "Machine Learning for Physicists" lecture series by Florian Marquardt
This project provides practical code examples that help physicists understand and apply machine learning techniques to their research problems. It takes foundational physics concepts and translates them into machine learning models, demonstrating how to use methods like t-SNE and work with datasets like MNIST. It's designed for physicists who want to incorporate machine learning into their analytical toolkit.
326 stars. No commits in the last 6 months.
Use this if you are a physicist looking for hands-on examples and code to learn how machine learning can be applied within physics contexts.
Not ideal if you are looking for a general-purpose machine learning library or production-ready code for non-physics applications.
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326
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177
Language
Jupyter Notebook
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Last pushed
May 05, 2025
Commits (30d)
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