Machine Learning for Fundamental Physics w/Ben Nachman, Staff Scientist at LBNL
Youtube : Download Convert to MP3
Description :
The goal of fundamental physics is to determine the building blocks of nature and how these components interact with each other. In order to achieve this goal, scientists have built enormous experiments to measure properties of particles interactions. These experiments are generating datasets that are comparable to some of the largest industrial da...
Related Videos :
![]() |
Beinan Wang: Data Access Patterns in AI and Analytics By: USFDataInstitute |
![]() |
David Gross - The Coming Revolutions in Fundamental Physics By: UC Berkeley Events |
![]() |
Deep learning for medical imaging applications By: Australian Data Science Network |
![]() |
Flow Matching for Generative Modeling (Paper Explained) By: Yannic Kilcher |
![]() |
Dr. Loïc Anderegg, “Laser Cooled Molecules for Quantum Science Applications”, Caltech, May 30, 2004 By: Farbod khoshnoud |
![]() |
Solving Natural Language Processing tasks with the Huggingface API By: USFDataInstitute |