Deep Learning with PyTorch : Neural Style Transfer
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.
Convolutional Neural Network
Neural Style Transfer
由 KD 提供2021年1月15日
It was a super informative project course, very well explained and demonstrated.
由 MM 提供2021年8月4日
A liitle more talk about the theoretical background of the paper would have been nice
由 IP 提供2022年1月6日
great guided project , learn NST, pytorch, vgg architecture before starting and there are some exceptions in the code feel free to search in stackoverflow.
由 JA 提供2021年2月21日
Excellent course with great structure, I will highly recommend this course and instructor was very knowledgeable.