Online Course
Image Noise Reduction with Auto-encoders using TensorFlow
Coursera Project Network via Coursera
Found in
Artificial Intelligence Courses
- Provider Coursera
- Cost Paid Course
- Session Upcoming
- Language English
- Certificate Paid Certificate Available
- Effort 5 hours worth of material
- Duration 1 weeks long
- Learn more about MOOCs
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Overview
Class Central Tips
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.
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.
Taught by
Amit Yadav
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