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Probability theoryStatisticsDeep LearningMachine LearningPythonMatrix Calculus Course Duration: 1 Hours
Course Instructor: Brian McCarthy
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 LearningMatrix CalculusProbability 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
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