Description
Generative Adversarial Networks (GANs): Complete Guide, the generative neural networks training course is published by Yudemy Academy. Neural networks are one of the most modern and attractive technologies in the field of deep learning and computer vision. They have attracted a lot of attention because they can produce fake content. One of the most classic examples is the creation of people who don’t exist in the real world to be used in TV shows. This technology is considered a revolution in the field of artificial intelligence to produce high quality results and is one of the most popular and relevant topics today. In this course, you will get to know the basic principles and mainly the practical implementation of the most modern neural network architectures.
This course is a complete guide as it covers everything from the most basic concepts to the most modern and advanced techniques so that in the end you have all the tools you need to build your projects. To implement the projects, you will learn several different architectures of GANs, such as: DCGAN (Deep Convolutional Generative Adversarial Network), WGAN (Wassertein GAN), WGAN-GP (Wassertein GAN-Gradient Penalty), cGAN (conditional GAN), Pix2Pix (Image-to-Image), CycleGAN (Cycle-Consistent Adversarial Network), etc. During this course, we will use the Python programming language and Google Colab online, so you don’t have to worry about installing and configuring libraries on your own device.
What you will learn
- Understand the basic concepts about GANs
- Creating images of digits (0-9) using DCGAN and WGAN
- Converting satellite images to maps using Pix2Pix architecture
- Transforming zebras into horses using CycleGAN architecture
- Transition style between images
- Apply super resolution to improve image quality using ESRGAN architecture
- Creating new faces of people with better quality and details using StyleGAN
- Generating images through textual descriptions
- Retrieving old photos using GFP-GAN
- Completing missing parts of images using Boundless architecture
- Generating deepfake for face swapping with SimSwap
Who is this course suitable for?
- People interested in building complex applications using GANs
- Graduate students taking courses in computer vision, artificial intelligence, digital image processing, or computer vision.
- People who want to implement their projects using computer vision techniques
- Data scientists who want to enhance their project portfolio
Course specifications Generative Adversarial Networks (GANs): Complete Guide
Head of the course seasons on 2023-7
Course prerequisites
- Programming logic
- Basic Python programming
- Knowledge about neural networks is desirable, but not mandatory
Pictures
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