Explanation
Deep Learning Image Generation with GANs and Propagation Models is a training course on building a random image system based on deep learning using generative networks (GAN) and diffusion models published by Udemy Academy. Intelligent image generation and recognition systems are equipped with various capabilities such as creating realistic and natural faces, image resolution and image resolution enhancement, face mask removal, etc. Photo production systems have had a rough ride so far. Just 10 years ago, it was not possible to generate three or four random images, but with today’s modern methods and software, people can easily create several thousand random images in a few minutes. . New systems have continued and even users can find the image they need by specifying the face of the text.
During the learning process of this course, you will use the powerful version 2 library of Tensorflow.
In Deep Learning Image Generation with GANs and Propagation Models you will learn:
- Learning how to work with a variable autoencoder
- Automatically create images with a variable autoencoder
- Generative Adversarial Networks
- Tensorflow library
- Create quality and professional images with ProGAN
- Development of a face mask extraction system with CycleGANs
- Metaresolvability and improved resolution of heterogeneous images with SRGANs
- And…
Course guidelines
Publisher: Udemy
Instructors: Neurallearn Dot AI
Language: English
Level: Introductory to Advanced
Number of Lessons: 27
Duration: 10 hours and 7 minutes
course topics
Image Generation for Deep Learning with GANs and Propagation Model Requirements
Basic knowledge of Python
Basic knowledge of Tensorflow
Access to the Internet, as we use Google Colab (free version)
Pictures
Deep Learning Image Generation with GANs and Model Diffusion Introductory video
installation guide
After the launch, follow your favorite player
Subtitle: None
Quality: 720p
download link
Password file: free download software
size
4.88 GB