Deep Learning with PyTorch : Neural Style Transfer
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
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
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:
Set google colab runtime
Loading VGG-19 pretrained model
Preprocess Image
Deprocess Image
Create content and style loss
Get content,style features and create gram matrix
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
Reviews
TOP REVIEWS FROM DEEP LEARNING WITH PYTORCH : NEURAL STYLE TRANSFER
The understanding in this course is amazing and very satisfying. I will recommended to my friends to take this one.
Excellent course with great structure, I will highly recommend this course and instructor was very knowledgeable.
It was a super informative project course, very well explained and demonstrated.
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