Master Generative Adversarial Networks (GANs) in no time
This course is a comprehensive guide to Generative Adversarial Networks (GANs). The theories are explained in depth and in a friendly manner. After each theoretical lesson, we will dive together into a hands-on session, where we will be learning how to code different types of GANs in PyTorch, which is a very advanced and powerful deep learning framework!
What you’ll learn
- Understand all the theoretical aspects in Generative Adversarial Networks (GANs).
- Master the practical skills in coding the Generative Adversarial Networks (GANs).
Course Content
- Introduction –> 1 lecture • 6min.
- Introduction to Generative Adversarial Networks –> 3 lectures • 29min.
- Deep Convolution Generative Adversarial Networks (DCGANs) –> 6 lectures • 1hr.
- Least Square GANs –> 1 lecture • 5min.
- Conditional GANs –> 2 lectures • 20min.
- Coupled GANs –> 2 lectures • 9min.
- Super Resolution GANs –> 2 lectures • 12min.
- Cycle GANs –> 2 lectures • 20min.
- Other Types of GANs –> 1 lecture • 10min.
Requirements
- Understanding the fundamentals of neural networks, convolutional neural networks, deep learning..
- Basic Programming experience (preferably in Python).
- Basic High-school Mathematics.
This course is a comprehensive guide to Generative Adversarial Networks (GANs). The theories are explained in depth and in a friendly manner. After each theoretical lesson, we will dive together into a hands-on session, where we will be learning how to code different types of GANs in PyTorch, which is a very advanced and powerful deep learning framework!
The following topics will be included:
DCGANs
LSGANs
CGANs
CoGANs
SRGANs
CycleGANs
other types of GANs
Each type will include a theoretical and practical session.
