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
Instructor
![Mustafa Qamaruddin](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 98 Reviews
- 112,462 Students
- 2 Courses
In 2011 started my software development career and contributed to large-scale products. Harnessed experiences in backend and cloud technologies and worked with international teams.