Image Super Resolution Using Autoencoders in Keras

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Understand what autoencoders are and why they are used

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

Clock1.5
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Cloud다운로드 필요 없음
Video분할 화면 동영상
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Laptop데스크톱 전용

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.

개발할 기술

Data ScienceDeep LearningMachine LearningComputer Visionkeras

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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