Image Super Resolution Using Autoencoders in Keras

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在此指导项目中,您将:

Understand what autoencoders are and why they are used

Design and train an autoencoder to increase the resolution of images with Keras

Clock1.5 hours
Advanced高级设置
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.

您要培养的技能

  • Data Science
  • Deep Learning
  • Machine Learning
  • Computer Vision
  • keras

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Project Overview and Import Libraries

  2. What are Autoencoders?

  3. Build the Encoder

  4. Build the Decoder to Complete the Network

  5. Create Dataset and Specify Training Routine

  6. Load the Dataset and Pre-trained Model

  7. Model Predictions and Visualizing the Results

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

授课教师

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