The Complete GANs Bootcamp: Theory and Applications

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.

The Complete GANs Bootcamp: Theory and Applications

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.

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