Image Noise Reduction with Auto-encoders using TensorFlow
4721 ya inscrito
4721 ya inscrito
In this 2-hour long project-based course, you will learn the basics of image noise reduction with auto-encoders. Auto-encoding is an algorithm to help reduce dimensionality of data with the help of neural networks. It can be used for lossy data compression where the compression is dependent on the given data. This algorithm to reduce dimensionality of data as learned from the data can also be used for reducing noise in data. 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 Tensorflow pre-installed. Note: 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 NL7 de abr. de 2020
Really great learning for beginners. Through project learning it gives very good confidence. But rhyme desktop should be available until completion of project.
por RB16 de abr. de 2020
A nice and short project and a good way to built a simple autoencoder and neural network classifier and getting them up and running.
por NS15 de ago. de 2020
nice presentation skill, it is helpful for me to noise reduction and image processing
por KO11 de oct. de 2020
Teachable and Readable course.
Thanks so much!!