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.