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

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

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

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About the author Denis Volkhonskiy

I’m an Artificial Intelligence researcher and working for a PhD thesis. My main area of interest is Deep Learning for Computer Vision and 3D Data Processing.

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

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: 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.

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

  • Author: Denis Volkhonskiy
  • Language: English
  • Category: Math
  • Status: Available
  • Platform: Udemy
  • Ranking: 4.2 points.

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What is included in this Udemy course?

Generative Adversarial Networks State of the art Generative Learning (2019) Progressively Growing GANs BIG Generative Adversarial Networks

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