Modern Generative Adversarial Networks

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Video: .MP4, 1280x720 | Audio: AAC, 44kHz, 2ch | Duration: 1h 58m
Genre: eLearning | Language: English | Size: 1,2 GB

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.
Screenshots

rapidgator_net:
https://rapidgator.net/file/03907854f4e3b79a0ea22114084f615e
https://rapidgator.net/file/6ff86e6786fcbc22fde7a98388daff52

nitroflare_com:
http://nitroflare.com/view/31D3A5A78155A09/Modern_Generative_Adversarial_Networks.part1.rar
http://nitroflare.com/view/86987FA927C3FB2/Modern_Generative_Adversarial_Networks.part2.rar

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