YoVDO

Deepsets, Graph Neural Networks, and Transformers in Machine Learning - Lecture 9

Offered By: International Centre for Theoretical Sciences via YouTube

Tags

Deep Learning Courses Machine Learning Courses Neural Networks 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 on Deepsets, Graph Neural Networks, and Transformers. Delve into the intricacies of these powerful algorithms as Sanmay Ganguly presents the ninth installment of a comprehensive series on statistical methods and machine learning in high energy physics. Gain valuable insights into cutting-edge approaches for analyzing complex data structures and relationships, essential for tackling the challenges in modern high energy physics research. Learn how these advanced neural network architectures can be applied to process and interpret the massive datasets generated by experiments like the Large Hadron Collider. Enhance your understanding of deep learning techniques that are revolutionizing data analysis in particle physics and beyond.

Syllabus

Lectures on Deepsets, Graph Neural Network and Transformers.. (Lecture 9) by Sanmay Ganguly


Taught by

International Centre for Theoretical Sciences

Related Courses

Физика как глобальный проект
National Research Nuclear University MEPhI via Coursera
Introduction to Quantum Field Theory (Theory of Scalar Fields) - Part 2
IIT Hyderabad via Swayam
Deep Learning Pipelines for High Energy Physics Using Apache Spark and Distributed Keras
Databricks via YouTube
Helium Dimers and Trimers - From Imaging of Structure to Movies of Ultrafast Dynamics - Reinhard Dorner
Kavli Institute for Theoretical Physics via YouTube
Bosons and Multi-Component Fermions Near Unitarity - Ubirajara van Kolck
Kavli Institute for Theoretical Physics via YouTube