Book Image

Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu
Book Image

Machine Learning for Mobile

By: Revathi Gopalakrishnan, Avinash Venkateswarlu

Overview of this book

Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Question and Answers
Index

Key ML mobile applications 


In this section, we will look at some of the most popular mobile applications and understand what they are doing in the field of mobile ML.

Facebook

Facebook has developed an AI platform, Caffe2Go. Through this toolset, Facebook initially wanted to provide enriched AI and AR experiences to users. They are enabling users to process videos and images through on-device ML and perform certain tasks without having to transmit these videos and images to the backend for complex image and video processing. Their style transfer toolkit enables users to take the artistic qualities of one image style, and apply it to other images and videos.

Google Maps

Google has introduced TensorFlow Lite as well as ML Kit that enables users to perform mobile ML in mobile applications. Google Maps from Google is a classic example of ML on mobile.

Snapchat

Snapchat is innovating on complex ML algorithms that are able to perceive facial features on an image captured by the camera. These algorithms...