General Adversarial Networks, or GANs, are powerful neural networks that you have likely already seen in action. In this course, Literacy Essentials: Core Concepts Generative Adversarial Networks, you’ll learn the main idea behind GANs. First, you’ll explore the basics of generator and discriminator networks. Next, you’ll discover how to incorporate these networks to create a GAN. Finally, you’ll learn how to apply GANs to solve real-world issues such as image captioning, and complex classification problems. When you’re finished with this course, you’ll have the skills and knowledge of GANs needed to understand how they can be a great addition to your Machine Learning solutions library.
Author Name: Jerry Kurata
Author Description:
Jerry has Bachelor of Science degrees in Geology and Physics. His plans to work in the oil exploration industry were sidetracked when he discovered he preferred to work with computers on simulation and data processing, instead of reading mud and core samples in the North Sea. His love of computers and tech resulted in him spending many additional hours working on computers while getting his Master’s degree in Computer Science. His current areas of interests include Machine Learning, Big Data,… more

Table of Contents

• Course Overview
  1min
• GAN Basics
  7mins
• How GANs Work
  12mins
• Using GANs to Solve Problems
  5mins
• Exploring Example GANs
  5mins