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 TM 提供2020年7月1日
good experience. very clear explanations. I liked it and recommend it for anyone who wants to understand and experience autoencoder basics.
由 MS 提供2020年5月7日
Well taught. Thanks. Please mail me data of the project. I need to revisit the code.
由 SR 提供2020年5月5日
Excellent ! New concept and definitely not boring. The instructor was able to explain most of the necessary elements but some functions were never explained.
由 DG 提供2020年6月15日
The theory explanation was succinct and satisfactory. The coding style and code explanation could have been better. Overall good for introduction to autoencoder.