Introduction to Generative Adversarial Networks with PyTorch

What you'll learn:
How Generative Adversarial Networks work internally
How to implement state of the art GANs techniques and methods using PyTorch
How to improve the training stability of GANs

Requirements
Familiarity with Python Programming
Familiarity with Deep Learning Concepts

Description
Master the basic building blocks of modern generative adversarial networks with a unique course that reviews the most recent research papers in GANs and at the same time gives the learner a very detailed hands-on experience in the topic. Start by learning the very basics of how GANs work and incrementally learn more cleverly crafted techniques that enhance your models from the basic GANs towards the more advanced Progressive Growing of GANs. On the journey, you shall learn a fair amount of deep learning concepts with an adequate discussion of the mathematics behind the modern models.

Who this course is for
Data scientists willing to take their skills to the next level in the area of GANs
Research / Postgraduate Students willing to get a comprehensive overview of recent advancement made in the area of GANs
Deep Learning practitioners willing to apply GANs at work in production environments
Enthusiasts willing to stay up to date on GANs research and development
Deep learning beginners willing to master the building blocks of modern GANs


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