Generative Adversarial Networks A-Z

Learn Generative Adversarial Networks with PyTorch

• Free tutorial
• Rating: 4.3 out of 54.3 (87 ratings)
• 2,271 students
• 1hr 59min of on-demand video
• Created by Denis Volkhonskiy
• English

What you’ll learn

• Generative Adversarial Networks
• State of the art Generative Learning
• Progressively Growing GANs
• BIG Generative Adversarial Networks

Requirements

• Probability theory, Statistics
• Machine Learning, Deep Learning
• Python
• Matrix Calculus

Description

I really love Generative Learning and Generative Adversarial Networks. These amazing models can generate high-quality images (and not only images). I am an AI researcher, and I would like to share with you all my practical experience with GANs.

Generative Adversarial Networks were invented in 2014 and since that time it is a breakthrough in Deep Learning for the generation of new objects. Now, in 2019, there exists around a thousand different types of Generative Adversarial Networks. And it seems impossible to study them all.

I work with GANs for several years, since 2015. And now I can share with you all my experience, going from the classical algorithm to the advanced techniques and state-of-the-art models. I also added a section with different applications of GANs: super-resolution, text to image translation, image to image translation, and others.

This course has rather strong prerequisites:

• Deep Learning and Machine Learning
• Matrix Calculus
• Probability Theory and Statistics
• Python and preferably PyTorch

Here are tips for taking most from the course:

1. If you don’t understand something, ask questions. In case of common questions, I will make a new video for everybody.
2. Use handwritten notes. Not bookmarks and keyboard typing! Handwritten notes!
3. Don’t try to remember all, try to analyze the material.

Who this course is for:

• People, who already know Deep Learning and want to study Generative Adversarial Networks from A to Z
• People, who know GANs, but wants to be in the front of the science

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Course content

4 sections • 21 lectures • 1h 59m total lengthCollapse all sections

Introduction2 lectures • 14min

• Introduction to Generative Adversarial Networks05:25
• Generative Learning and Density Estimation08:19

Original Generative Adversarial Networks5 lectures • 32min

• Generative Adversarial Networks04:49
• Algorithm of Training for Generative Adversarial Networks05:13
• PRACTICE #1: 1-dimentional GANs00:15
• PRACTICE #1 SOLUTION: 1-dimentional GANs with PyTorch15:55
• PRACTICE #2: 2-dimentional GANs. Mode Collapse05:20

Deep Convolutional Generative Adversarial Networks9 lectures • 41min

• Motivation to Convolutions01:32
• Convolution operation02:30
• Convolution Parameters02:42
• Transposed Convolutions05:16
• Deep Convolutional Generative Adversarial Networks04:36
• Measures of Quality for GANs08:52
• PRACTICE #3: Generating FACES part 108:37
• PRACTICE #3: Generating FACES part 204:42
• PRACTICE #3: Generating FACES part 301:55

Applications of Generative Adversarial Network5 lectures • 34min

• Applications of Generative Adversarial Network04:45
• Image to image translation with Cycle GANs07:01
• Image Super-Resolution07:13
• Patch-Based Image Inpainting04:41
• Generation of images from text09:55

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