Image Super Resolution Using Autoencoders in Keras

4.4
326 個評分
提供方
Coursera Project Network
8,424 人已註冊
在此指導項目中,您將:

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高級設置
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

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

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

授課教師

審閱

來自IMAGE SUPER RESOLUTION USING AUTOENCODERS IN KERAS的熱門評論

查看所有評論

常見問題

常見問題

還有其他問題嗎?請訪問 學生幫助中心