Udemy – Image Super-Resolution GANs 2022-1

Udemy – Image Super-Resolution GANs 2022-1 Downloadly IRSpace

Apr 17, 2025 - 12:30 Updated: Apr 21, 2025 - 04:43

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Udemy – Image Super-Resolution GANs 2022-1

Image Super-Resolution GANs is a training course on increasing the quality and size of images through artificial intelligence and adversarial generative networks with the Python programming language and the Tensorflow library version 2, which was published by Udemy Academy. Before the arrival of artificial intelligence, our only idea of increasing the quality and size of images was going back to the world of movies and crime series. Where law enforcement officers used computers to enlarge small photos and identify suspects. Meta-decomposition is one of the newest techniques for increasing the size of images, which has been used and exploited by many professionals. The system designed in this training course will be able to enlarge an image up to four times its original size or increase the number of pixels by 16 times.

In this training course, we will use Python programming language and TensorFlow version 2 library to multiply the size of an image. Training artificial neural networks is a very time-consuming and complex process and consumes a lot of hardware and software resources. In this regard, we will use the free Google CoLab service.

What you will learn in Image Super-Resolution GANs:

Building a productive artificial intelligence to increase the size and quality of various images
Creating a distinctive architecture to remove the quality and depth of the original image
Using the Google CoLab service to perform initial processing
And …

Course specifications

Publisher: Udemy
Instructors: Brad Klingensmith
Language: English
Level: Intermediate
Number of Lessons: 24
Duration: 2 hours and 31 minutes

Course topics

Image Super-Resolution GANs Content

Image Super-Resolution GANs Prerequisites

My “High Resolution Generative Adversarial Networks (GANs)” course
Python experience
Convolutional neural network experience
Basic familiarity with TensorFlow 2.0 and Keras