Deep Learning with PyTorch : Neural Style Transfer

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Coursera Project Network
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In this Free Guided Project, you will:

Understand Neural Style Transfer Practically

Be able to create artistic style image by applying style transfer using pytorch

Showcase this hands-on experience in an interview

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

In this 2 hour-long project-based course, you will learn to implement neural style transfer using PyTorch. Neural Style Transfer is an optimization technique used to take a content and a style image and blend them together so the output image looks like the content image but painted in the style of the style image. We will create artistic style image using content and given style image. We will compute the content and style loss function. We will minimize this loss function using optimization techniques to get an artistic style image that retains content features and style features. This guided project is for learners who want to apply neural style transfer practically using PyTorch. In order to be successful in this guided project, you should be familiar with the theoretical concept of neural style transfer, python programming, and convolutional neural networks.A google account is needed to use the Google colab environment.

Skills you will develop

Convolutional Neural NetworkDeep LearningpytorchNeural Style Transfer

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. Set google colab runtime

  2. Loading VGG-19 pretrained model

  3. Preprocess Image

  4. Deprocess Image

  5. Create content and style loss

  6. Get content,style features and create gram matrix

  7. Training loop

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



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Frequently Asked Questions

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