Udemy - Deep Learning Introduction to GANs

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
Requirements
It is recommended that you know Python and the basics of Tensorflow
You need to have an intermediate understanding on Neural Networks and the math behind them
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 a GAN into its parts and analyzing them. Then we will look at the loss functions we will be using and the Frechet Inception Distance. Finally we will take all this new information and apply it using Python and Tensorflow to the MNIST dataset. The code will be written such that you can use it for any of your image-based projects.
Who this course is for:
People who have never worked with GANs and want to learn it
People who want to get a GAN framework that they can use right away
People who want to generate more data for their machine learning models

Homepage

https://www.udemy.com/course/deep-learning-introduction-to-gans/