Image Super Resolution Using Autoencoders in Keras
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.
由 AZ 提供Jun 16, 2020
Very informative, but extremely short project, I would have loved for more explanation on the theory behind each of the layers used and more loss functions and optimizer.
由 IS 提供Jun 9, 2020
great course, implement quite well on the platform. A little more elaborate implementation and some more theory would be great.
由 KT 提供May 27, 2020
Amazing course to gain knowledge in one of the trending field i.e. Image Super Resolution. I gain what I was looking for in this particular guided project.
由 MS 提供May 7, 2020
Well taught. Thanks. Please mail me data of the project. I need to revisit the code.