Generative Adversarial Networks A-Z: State of the art (2019)

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

Generative Adversarial Networks were invented in 2014 and since that time it is a breakthrough in the Deep Learning for generation of new objects. Now, in 2019, there exists around a thousand of 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 application 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

  
  

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 analyse the material.