Image Noise Reduction with Auto-encoders using TensorFlow

Level:All Levels
InstructorAmit Yadav
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Course description
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.

Meet your instructors:

  • Amit Yadav photoAmit Yadav
    Machine Learning Instructor
    Amit is a Machine Learning Engineer with focus in creating deep learning based computer vision and signal processing products. He has led chat bot development at a large corporation in the past. Amit is one of the Machine Learning and Data Science instructors at Rhyme.




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