Generative Adversarial Networks A-Z

Generative Adversarial Networks

State of the art Generative Learning

Progressively Growing GANs

BIG Generative Adversarial Networks


  • Probability theory, Statistics
  • Machine Learning, Deep Learning
  • Python
  • Matrix Calculus


I really love Generative Learning and Generative Adversarial Networks. These amazing models can generate high-quality images (and not only images). I am an AI researcher, and I would like to share with you all my practical experience with GANs.

Generative Adversarial Networks were invented in 2014 and since that time it is a breakthrough in Deep Learning for the generation of new objects. Now, in 2019, there exists around a thousand different types of Generative Adversarial Networks. And it seems impossible to study them all.

I work with GANs for several years, since 2015. And now I can share with you all my experience, going from the classical algorithm to the advanced techniques and state-of-the-art models. I also added a section with different applications of GANs: super-resolution, text to image translation, image to image translation, and others.

This course has rather strong prerequisites:

  • Deep Learning and Machine Learning
  • Matrix Calculus
  • Probability Theory and Statistics
  • Python and preferably PyTorch

Here are tips for taking most from the course:

  1. If you don’t understand something, ask questions. In case of common questions, I will make a new video for everybody.
  2. Use handwritten notes. Not bookmarks and keyboard typing! Handwritten notes!
  3. Don’t try to remember all, try to analyze the material.

Who this course is for:

  • People, who already know Deep Learning and want to study Generative Adversarial Networks from A to Z
  • People, who know GANs, but wants to be in the front of the science

Course content