Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images.
Image Super Resolution Using Autoencoders in Keras
Taught in English
Instructor: Snehan Kekre
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(338 reviews)
What you'll learn
Understand what autoencoders are and why they are used
Design and train an autoencoder to increase the resolution of images with Keras
Skills you'll practice
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Recommended experience
(338 reviews)
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About this Guided Project
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 and Import Libraries
What are Autoencoders?
Build the Encoder
Build the Decoder to Complete the Network
Create Dataset and Specify Training Routine
Load the Dataset and Pre-trained Model
Model Predictions and Visualizing the Results
Recommended experience
Prior coding experience in Python, a working knowledge of Keras or Tensorflow, and an understanding of convolutional neural networks is recommended.
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Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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