Generative Adversarial Networks
Write a Neural Network algorithm that will generate realistic photographs of objects, nature and human faces!
This title is part of the Deep Learning Mini-Degree
Generative Adversarial Networks (GANs) are a type of artificial intelligence algorithm, which has 2 different Neural Networks compete against each to gain knowledge. Introduced in 2014 by Ian Goodfellow, this technique can be successfully used to generate realistic photographs of objects, nature and even human faces. Other applications include removing noise from astrophysical images, generating new fake data for other neural networks, or even enhancing photos. You know how in movies, the FBI always does that cool zoom on a photo of a suspect? GANs can be used to actually do that!
This course begins with the basics and intuition of GANs, introducing the the 2 types of Models – Discriminative and Generative – and their specific tasks in the algorithm. Continuing through this course, you’ll learn the difference between regular GANs, DCGANs (Deep Convolutional GANs) and AC-GANs (Auxiliary Classifer GANs), and how to implement them using Python.
What you’ll learn:
- Classifying the data as real or artificially generated through a Discriminator
- Fooling the Discriminator into believing generated data is real via a Generator
- Analyzing the problem using Game Theory
- Training a GAN – including an intuition of the algorithm and the maths behind it
- Tips and tricks – normalizing data, optimization, label smoothing and more
- Challenges of training GANs – mode colapse, counting, perspective and Global Structure
About the Deep Learning Mini-Degree
The Deep Learning Mini-Degree is an on-demand learning curriculum composed of 6 professional-grade courses geared towards teaching you how to solve real-world problems and build innovative projects using Machine Learning and Python. Learn and understand the fundamentals necessary to build the next generation of intelligent applications and software, with concepts and theory that can be applied across technology and frameworks.
Challenge yourself by joining this exciting project-based curriculum and gain the knowledge and abilities required to succeed in this brand new industry. No prior experience with AI or Machine Learning is necessary to join. However, basic to intermediate Python skills are assumed in all of the courses.
- Intermediate Python skills
- Completion of Convolutional Neural Networks Course (or similar knowledge)