Style transfer refers to the use of a neural network to transform an image so that it comes to artistically resemble another image, while still retaining its original content. Neural style transfer is fast becoming popular as a way to change the aesthetics of an image. In this course, Style Transfer with PyTorch, you will gain the ability to use pre-trained convolutional neural networks (CNNs) that come out-of-the-box in PyTorch for style transfer. First, you will learn how style transfer involves a style image as well as a content image, and a pretrained neural network that usually does not change at all during the training process. Next, you will discover how intermediate layers of the CNN are designated as style layers of interest and content layers of interest. Then, you will explore the minimization of two loss functions – a style loss and a content loss. Finally, you will delve into leveraging a new and much-hyped family of ML models, known as Generative Adversarial Networks (GANs) to create realistic images and videos. When you’re finished with this course, you will have the skills and knowledge to perform neural style transfer to get images that combine content and artistic style from two different inputs and use GANs to generate realistic images from noise.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more

Table of Contents

• Course Overview
  2mins
• Understanding Neural Style Transfer
  38mins
• Implementing Neural Style Transfer in PyTorch
  33mins
• Building Generative Adversarial Networks in PyTorch
  35mins