Image Denoising Using AutoEncoders in Keras and Python
Understand the theory and intuition behind Autoencoders
Build and train an image denoising autoencoder using Keras with Tensorflow 2.0 as a backend
Assess the performance of trained autoencoders using various Key performance indicators
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.
Skills you will develop
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:
Project Overview
Import libraries and datasets
Perform data visualization
Perform data preprocessing
Understand the theory and intuition behind autoencoders
Build and train autoencoder model
Evaluate trained model performance
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
Reviews
TOP REVIEWS FROM IMAGE DENOISING USING AUTOENCODERS IN KERAS AND PYTHON
Just right for me at the moment I was doing it I just needed to do a light project in which I could gain some practical skills
This project has helped me to build m basic well towards image processing and I would recommend this course to everyone
My Cloud access was denied after a certain time.. I had to do the coding all over again in my notebook. Rest was good.
Clear explanation of auto encoders. This guided project was just right to get a good understanding of the topic
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