What you'll learn
Generative Adversarial Networks
State of the art Generative Learning (2019)
Progressively Growing GANs
BIG Generative Adversarial Networks
Requirements
Probability theory
Statistics
Deep Learning
Machine Learning
Python
Matrix Calculus
Description
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.
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

DOWNLOAD
uploadgig


rapidgator


nitroflare