Is the Ground State of Anderson's Impurity Model a Recurrent Neural Network?
Offered By: ICTP Condensed Matter and Statistical Physics via YouTube
Course Description
Overview
Explore the intriguing connection between Anderson's impurity model and recurrent neural networks in this 20-minute conference talk by Jonas RIGO from Forschungszentrum Jülich, Germany. Delve into the fundamental question of whether the ground state of Anderson's impurity model can be interpreted as a recurrent neural network. Gain insights into the intersection of condensed matter physics, statistical physics, and machine learning as the speaker presents their research findings and analysis on this thought-provoking topic.
Syllabus
Is the ground state of Anderson's impurity model a recurrent neural network?
Taught by
ICTP Condensed Matter and Statistical Physics
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX