Neural Style Transfer with TensorFlow

In this 2-hour long project-based course, you will learn the basics of Neural Style Transfer with TensorFlow. Neural Style Transfer is a technique to apply stylistic features of a Style image onto a Content image while retaining the Content's overall structure and complex features. We will see how to create content and style models, compute content and style costs and ultimately run a training loop to optimize a proposed image which retains content features while imparting stylistic features from another image. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

• Neural Style Transfer
• Welcome to this project-based course on Neural Style Transfer with TensorFlow. In this project, you will apply a style image's stylistic features to a content image while retaining the overall structure of the content image, and you will do this with the help of a neural network. Neural Style Transfer is a technique to apply stylistic features of a Style image onto a Content image while retaining the Content’s overall structure and complex features. We will see how to create content and style models, compute content and style costs, and ultimately run a training loop to optimize a proposed image which retains content features while imparting stylistic features.