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:
- If you don’t understand something, ask questions. In case of common questions, I will make a new video for everybody.
- Use handwritten notes. Not bookmarks and keyboard typing! Handwritten notes!
- 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
Show less
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