Deep Learning: Introduction to GANs
Rating 4.0 out of 5 (6 ratings in Udemy)
What you'll learn
- Understand the principles of GANs and how they work internally
- The mathematics behind four loss functions: Minimax, Non-Saturating, Least Squares, and Wasserstein
- How to determine the quality of the data a GAN produces
- How to generate numbers from the MNIST Dataset
- Apply GAN to new datasets
Description
In this course you will learn from scratch how to implement GANs to any of your projects. We will start with by breaking down …
Duration 2 Hours 58 Minutes
Paid
Self paced
Beginner Level
English (US)
42
Rating 4.0 out of 5 (6 ratings in Udemy)
Go to the Course
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Paid
Self paced
Beginner Level
English (US)
42
Rating 4.0 out of 5 (6 ratings in Udemy)
Go to the Course