Coupon Waali 
- Probability theory
- Statistics
- Deep Learning
- Machine Learning
- Python
- Matrix Calculus
How to generate high-quality images from noise? Is it really possible?
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:
- If you don’t understand something, ask questions. In case of common questions I will make a new video for everybody.
- Use handwritten notes. Not bookmarks and keyboard typing! Handwritten notes!
- Don’t try to remember all, try to analyse the material.
- 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
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