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

An introduction to TensorFlow


TensorFlow is a tool to implement machine learning developed by Google, and was open sourced in 2015. It is a product that can be installed on desktops and can be used to create machine learning models. Once the model has been built and trained on the desktop, the developer can transfer these models to mobile devices and start using them to predict results in mobile applications by integrating them into iOS and Android mobile applications. There are currently two flavors of TensorFlow available for implementing machine learning solutions on mobile and embedded devices:

  • Mobile devices: TensorFlow for Mobile
  • Mobile and Embedded devices: TensorFlow Lite 

The following table will help you to understand the key differences between TensorFlow for mobile and TensorFlow Lite:

TensorFlow for Mobile

TensorFlow Lite

Designed to work with larger devices.

Designed to work with really small devices.

Binary is optimized for mobile.

Binary is really very small in size optimized for...