This module is intended to provide a foundation in energy-based models, and is a part of the Deep Learning Course at NYU's Center for Data Science, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Introduction to Deep Learning (module 1 of the course), Parameter sharing (module 2 of the course), and an Introduction to Data Science or a Graduate Level Machine Learning course.
Deep Learning: Foundations of Energy-Based Models
Course Features
Videos of lectures
Lecture slides
Lecture notes
Homework exercises
Tutorials
Discussion session
Lessons of this Course
1
2
Duration:
1:01:04
Speaker:
This lecture covers the concept of inference in latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.
2
3
Duration:
10:42
Speaker:
This panel discussion covers how energy based models are used and is a part of the Deep Learning Course at NYU's Center for Data Science.
3
3
Duration:
1:51:30
Speaker:
This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy-based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.
4
4
Duration:
1:48:53
Speaker:
This lecture is a foundationational lecture for the concept of energy-based models with a particular focus on the joint embedding method and latent variable energy based models (LV-EBMs) and is a part of the Deep Learning Course at NYU's Center for Data Science.
5
5
Duration:
1:04:48
Speaker:
This tutorial covers the concept of training latent variable energy based models (LV-EBMs) and is is a part of the Deep Learning Course at NYU's Center for Data Science.
