Literacy Essentials: Core Concepts Generative Adversarial Network
Offered By: Pluralsight
Course Description
Overview
This course will teach you the theory and code behind General Adversarial Networks (GANs). GANs are self-evaluating and self-improving networks that can create the stunning results you see in generated photos, videos, and sounds.
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.
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.
Syllabus
- Course Overview 1min
- GAN Basics 7mins
- How GANs Work 12mins
- Using GANs to Solve Problems 5mins
- Exploring Example GANs 5mins
Taught by
Jerry Kurata
Related Courses
Introduction to Artificial IntelligenceStanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent