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Image Super Resolution Using Autoencoders in Keras

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Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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Dive into the fascinating world of Image Super Resolution with this hands-on project focused on utilizing Autoencoders in Keras. Presented by Coursera, this intermediate-level course offers a succinct yet comprehensive learning experience over just 1.5 hours. Ideal for IT and Computer Science enthusiasts, you will explore advanced techniques to enhance image quality through practical exercises. This free course is perfect for those looking to deepen their understanding of machine learning applications in image processing. Join now and elevate your skills in a highly relevant and cutting-edge domain, guided by expert instructors.