Style Transfer with PyTorch

Course Overview

By taking this course, learners will gain a comprehensive understanding of the important aspects of neural style transfer and Generative Adversarial Networks. They will be able to use pre-trained convolutional neural networks (CNNs) that come out-of-the-box in PyTorch for style transfer, and learn how to designate intermediate layers of the CNN as style layers of interest and content layers of interest. Learners will also be able to minimize two loss functions - a style loss and a content loss, and leverage GANs to create realistic images and videos.


Possible Development Paths include becoming a computer vision engineer, a machine learning engineer, or a data scientist. Learners can also pursue further studies in computer vision, machine learning, and deep learning.


Learning Suggestions for learners include taking courses related to computer vision, machine learning, and deep learning. Learners should also practice coding and build projects to gain hands-on experience. They should also stay up-to-date with the latest advancements in the field by reading research papers and attending conferences.