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Coursera Project Network

Image Denoising Using AutoEncoders in Keras and Python

Coursera Project Network via Coursera


In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Autoencoders
- Import Key libraries, dataset and visualize images
- Perform image normalization, pre-processing, and add random noise to images
- Build an Autoencoder using Keras with Tensorflow 2.0 as a backend
- Compile and fit Autoencoder model to training data
- Assess the performance of trained Autoencoder using various KPIs

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.


  • Untitled Module
    • In this hands-on project, we will train an autoencoder to remove noise from grayscale images. In this practical project we will go through the following tasks: (1) Project Overview, (2) Import libraries and datasets, (3) Perform data visualization, (4) Perform data pre-processing, (5) Understand the theory and intuition behind autoencoders, (6) Build and train autoencoder model, (7) Evaluate trained model performance

Taught by

Ryan Ahmed


4.5 rating at Coursera based on 274 ratings

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