For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai
To follow along with the course, visit the course website:
https://deepgenerativemodels.github.io/
Stefano Ermon
Associate Professor of Computer Science, Stanford University
https://cs.stanford.edu/~ermon/
Learn more about the online course and how to enroll: https://online.stanford.edu/courses/cs236-deep-generative-models
To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/
Chapters:
00:00 - Introduction
00:20 - Course Background and Evolution
00:36 - Importance of Generative Models in Industry
00:47 - Course Goals and Learning Outcomes
01:31 - Challenges Addressed by Generative Models
02:07 - Understanding High-Dimensional Signals
02:45 - Generative Model Foundations
03:30 - Philosophy Behind Generative Models
04:06 - Generative vs. Traditional Models
04:49 - Building Data Simulators
05:32 - Probability Distributions Overview
06:55 - Inverse Problems in Generative Modeling
08:10 - Applications in Image and Text Generation
09:01 - Overview of Video Generation
09:39 - Generative Models and Context
10:14 - Natural Control Signals for Generative Processes
11:10 - Generative Models in Medicine
12:14 - Progress in Generative Models
12:59 - Introduction to AI in Healthcare
13:50 - Applications in Robotics and Decision-Making
15:00 - Mastering Generative Models
19:40 - Project Opportunities and Expectations
21:34 - Course Requirements
22:58 - Logistics and Resources
27:10 - Encouragement for Exploration and Innovation
28:30 - Importance of Theory in Generative Models
29:12 - Emphasis on Coding and Programming Skills
31:45 - Conclusion and Final Thoughts