Book Image

Machine Learning Projects for Mobile Applications

By : Karthikeyan NG
Book Image

Machine Learning Projects for Mobile Applications

By: Karthikeyan NG

Overview of this book

Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google’s ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN. By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.
Table of Contents (16 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Artistic neural style transfer


Image transformations are applied through fast style transfer. Let's dig deeper into how style transfer works before the implementation of our mobile application. Everyone loves to see their work in an artistic style. Artistic neural style transfer helps us see our own images in an art form that involves mixing your content and a style in order to introduce a unique visual experience. Before now, there was no AI-based system capable of implementing such a system. 

Look at the following screenshot for an example of how an artistic style is applied on a normal image:

The application we will be creating in this chapter is based on the implementation of a system similar to that shown in the preceding screenshot. There have been multiple papers published that prove its near-human performance of facial and object recognition. Deep neural networks help to implement artificial human vision. In this application, the algorithm that we use implements a deep neural network...