Back to Project: Image Noise Reduction with Auto-encoders using TensorFlow
Learner Reviews & Feedback for Project: Image Noise Reduction with Auto-encoders using TensorFlow by Rhyme
6 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.
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.
Filter by:
1 - 1 of 1 Reviews for Project: Image Noise Reduction with Auto-encoders using TensorFlow
By Nilesh N
•Mar 28, 2020
Crisp and useful!
© 2020 Coursera Inc. All rights reserved.
