Image Denoising Using AutoEncoders in Keras and Python

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In this Guided Project, you will:

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

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

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

Deep LearningArtificial Intelligence (AI)Machine LearningPython ProgrammingComputer Vision

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. Project Overview

  2. Import libraries and datasets

  3. Perform data visualization

  4. Perform data preprocessing

  5. Understand the theory and intuition behind autoencoders

  6. Build and train autoencoder model

  7. 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

Instructor

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Frequently Asked Questions

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