Image Denoising Using AutoEncoders in Keras and Python

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