YoVDO

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 2

Offered By: International Centre for Theoretical Sciences via YouTube

Tags

Machine Learning Courses Data Analysis Courses Neural Networks Courses Particle Physics Courses Transformers Courses High-Energy Physics Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
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.

Syllabus

Lectures on Deepsets, Graph Neural Network and Transformers... (Lecture 2) by Sanmay Ganguly


Taught by

International Centre for Theoretical Sciences

Related Courses

粒子世界探秘 Exploring Particle World
Shanghai Jiao Tong University via Coursera
Dark Side of the Universe
World Science U
Nature's Constituents
California Institute of Technology via World Science U
Физика тяжелых ионов
National Research Nuclear University MEPhI via Coursera
Particle Physics: an Introduction
University of Geneva via Coursera