Introduction to Generative Adversarial Networks with PyTorch

A comprehensive course on GANs including state of the art methods, recent techniques, and step-by-step hands-on projects

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.

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.

Course Content

  • Course Agenda –> 1 lecture • 8min.
  • Introduction to PyTorch for GANs –> 4 lectures • 36min.
  • Generate Handwritten Digits with Vanilla GAN –> 6 lectures • 1hr 8min.
  • Generate Specific Digits with Conditional GAN –> 5 lectures • 1hr 33min.
  • Diving Deeper with a Deep Convolutional GAN –> 4 lectures • 45min.
  • Generate Realistic Human Faces with Progressive GAN –> 3 lectures • 44min.
  • Generate Videos from Other Videos –> 7 lectures • 51min.
  • Appendix: Interesting Readings –> 4 lectures • 9min.

Introduction to Generative Adversarial Networks with PyTorch

Requirements

  • Familiarity with Python Programming.
  • Familiarity with Deep Learning Concepts.

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.

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