Back to Image Noise Reduction with Auto-encoders using TensorFlow
Learner Reviews & Feedback for Image Noise Reduction with Auto-encoders using TensorFlow by Coursera Project Network
75 ratings
About the Course
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.
Top reviews
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1 - 10 of 10 Reviews for Image Noise Reduction with Auto-encoders using TensorFlow
By Narendra L L
•Apr 08, 2020
Really great learning for beginners. Through project learning it gives very good confidence. But rhyme desktop should be available until completion of project.
By Ravi P B
•Apr 17, 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.
By Nilesh N
•Mar 28, 2020
Crisp and useful!
By XAVIER S M
•Jun 02, 2020
Very Helpful !
By Sumit Y
•Jul 09, 2020
Fine !!
By sarithanakkala
•Jun 24, 2020
Useful
By p s
•Jun 23, 2020
Super
By tale p
•Jun 17, 2020
good
By Rohit M
•Jun 13, 2020
NICE COURSE :-))
By NAIDU P S A
•Jun 27, 2020
nice
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