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

Understanding the basics of Core ML


Core ML enables iOS mobile applications to run machine learning models locally on a mobile device. It enables developers to integrate a broad variety of machine learning model types into a mobile application. Developers do not require extensive knowledge of machine learning or deep learning to write machine learning mobile applications using Core ML. They just need to know how to include the ML model into the mobile app similar to other resources and use invoke it in the mobile application. A data scientist or a machine learning expert can create an ML model in any technology they are familiar with, say Keras, scikit-learn, and so on. Core ML provides tools to convert the ML data model created using other tools (tensor, scikit-learn, and so on) to a format that is mandated by Core ML. 

This conversion to a Core ML model happens during the app development phase. It does not happen in real time when the application is being used. The conversion is done by...