Book Image

Hands-On Java Deep Learning for Computer Vision

By : Klevis Ramo
Book Image

Hands-On Java Deep Learning for Computer Vision

By: Klevis Ramo

Overview of this book

Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The book is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy.
Table of Contents (8 chapters)

Neural style transfer

In this section, we are going to learn what neural style transfer is, the cost function, and what we are trying to minimize in the case of neural style transfer. When we are referring to style transfer, we will always have a content image followed by a style image, or the artwork, and what we want to do is to have a final image that looks such as the content image, but painted with a style from the styled image. Basically it's like the style is being transferred from the Van Gogh picture (as shown in the following diagram) to the content image:

Let's consider another example which is shown as follows. We have the content image once again, then we have the style image or the artwork, and we have a final, or the third image that looks like similar to both of those previous images, but in the same way they also look somewhat different:

In a few words...