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

Opportunities for stakeholders


This section provides details of the key stakeholders in the landscape who contribute and determine the success and spread of ML on mobile devices. It explores how they contribute to mobile ML and what innovations are being carried out by each of them to increase the acceptance of mobile ML and make it reach far and wide.

Hardware manufacturers

The hardware is the platform that forms the basis for executing ML mobile applications. ML has specific requirements in terms of processing units and memory in order to run the complex ML algorithms. Until recently hardware limitations was one reason that drove the majority of ML processing to be undertaken in backend servers where there are no limits on processing units or memory. But now, most device manufacturers are making groundbreaking innovations that render hardware suitable for running mobile on-device ML applications:

  • Apple has already designed and built a neural engine as part of its iPhone X's main chip set...