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Learn Generative Adversarial Networks with PyTorch
An excellent training about Math
Generative Adversarial Networks A-Z: State of the art (2019)
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 LearningMatrix CalculusProbability Theory and StatisticsHere 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.
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