Explore advanced machine learning techniques in this lecture focusing on Deepsets, Graph Neural Networks, and Transformers. Delve into the application of these powerful algorithms in the context of High Energy Physics research. Gain insights from expert Sanmay Ganguly as he discusses the potential of these methods for analyzing complex data structures and improving data-driven discoveries in particle physics. Learn how these cutting-edge approaches can be leveraged to process the massive datasets generated by experiments like the Large Hadron Collider, potentially uncovering new physics phenomena. Enhance your understanding of deep learning architectures specifically tailored for handling sets, graphs, and sequential data in scientific applications.
Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 2
International Centre for Theoretical Sciences via YouTube
Overview
Syllabus
Lectures on Deepsets, Graph Neural Network and Transformers... (Lecture 2) by Sanmay Ganguly
Taught by
International Centre for Theoretical Sciences
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