Image Denoising Using AutoEncoders in Keras and Python

About this guided project

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.

What skills will you gain?

Keras (Neural Network Library)

Tensorflow

Artificial Neural Networks

Deep Learning

Artificial Intelligence and Machine Learning (AI/ML)

Applied Machine Learning

Machine Learning