Description
Deep Learning Image Generation with GANs and Diffusion Model is a training course on building a random image generation system based on deep learning using competitive generative networks (GAN) and diffusion models published by Yudemy Academy. Intelligent image production and recognition systems are equipped with various capabilities such as creating completely realistic and natural faces, metadissolving and increasing image resolution, removing masks from faces, etc. Image production systems have traveled a tortuous path so far. Until just 10 years ago, it was not possible to produce random three by four images, but with today’s advanced systems and software, people can easily create several thousand random images in a few minutes. New systems have even gone further and users can get the image they need by describing a face in text.
During the educational process of this course, you will use the powerful Tensorflow version 2 library extensively.
What you will learn in the Deep Learning Image Generation with GANs and Diffusion Model training course:
- Getting to know the working process of the variable autoencoder
- Automatic creation of images with variable autoencoder
- Generative Adversarial Networks
- Tensorflow library
- Create quality and professional images with ProGAN
- Development of a system to remove the mask from the face with CycleGANs
- Meta-resolvability and improving the resolution of different images with SRGANs
- And …
Course details
Publisher: Yudmi
teacher: Neurallearn Dot AI
English language
Training level: introductory to advanced
Number of courses: 27
Training duration: 10 hours and 7 minutes
Course headings
Course prerequisites
Basic Knowledge of Python
Basic Knowledge of Tensorflow
Access to an internet connection, as we shall be using Google Colab (free version)
Course images
Course introduction video
Installation guide
After Extract, view with your favorite Player.
Subtitle: None
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
4.88 GB
Be the first to comment