Image Noise Reduction with Auto-encoders using TensorFlow

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In this Guided Project, you will:

Develop an understanding of how Auto encoders work.

Be able to apply an auto encoder to reduce noise in given images.

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

Data ScienceDeep LearningNoise ReductionMachine LearningAutoencoder

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Introduction and Importing Libraries

  2. Data Preprocessing

  3. Adding Noise

  4. Building and Training a Classifier

  5. Building the Autoencoder

  6. Training the Autoencoder

  7. Denoised Images

  8. Composite Model

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently asked questions

Frequently Asked Questions

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