Image Super Resolution Using Autoencoders in Keras
8715 ya inscrito
8715 ya inscrito
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.
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por KT27 de may. de 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.
por KK10 de jun. de 2020
It really helps me a lot. But I could not test with pretrained model. It's sad. Thank you so much.
por TM1 de jul. de 2020
good experience. very clear explanations. I liked it and recommend it for anyone who wants to understand and experience autoencoder basics.
por RR14 de sep. de 2020
UI of platform was very bad scrolling was very difficult. content was quite good.