YoVDO

Deep Generative Modeling

Offered By: Alexander Amini via YouTube

Tags

Deep Learning Courses Autoencoders Courses Variational Autoencoders Courses Generative Models Courses Latent Variable Models Courses Diffusion Models Courses

Course Description

Overview

Explore deep generative modeling in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into the importance of generative models, latent variable models, and autoencoders. Learn about variational autoencoders, including priors on latent distributions, the reparameterization trick, and applications in debiasing. Discover generative adversarial networks (GANs), their training process, and recent advances like conditional GANs and CycleGANs. Gain insights into the intuitions behind these powerful techniques and their practical applications. Conclude with a brief introduction to diffusion models, preparing you for the cutting edge of generative AI research.

Syllabus

​ - Introduction
- Why care about generative models?
​ - Latent variable models
​ - Autoencoders
​ - Variational autoencoders
- Priors on the latent distribution
​ - Reparameterization trick
​ - Latent perturbation and disentanglement
- Debiasing with VAEs
​ - Generative adversarial networks
​ - Intuitions behind GANs
- Training GANs
- GANs: Recent advances
- Conditioning GANs on a specific label
- CycleGAN of unpaired translation
​ - Summary of VAEs and GANs
- Diffusion Model sneak peak


Taught by

https://www.youtube.com/@AAmini/videos

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera
機器學習技法 (Machine Learning Techniques)
National Taiwan University via Coursera
Machine Learning Capstone: An Intelligent Application with Deep Learning
University of Washington via Coursera
Прикладные задачи анализа данных
Moscow Institute of Physics and Technology via Coursera
Leading Ambitious Teaching and Learning
Microsoft via edX