Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu

Buy this Book

Machine Learning for Mobile

By: Revathi Gopalakrishnan, Avinash Venkateswarlu

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Introduction to Machine Learning on Mobile

Definition of machine learning

The machine learning process

Types of learning

Why use machine learning on mobile devices?

Summary

Supervised and Unsupervised Learning Algorithms

Introduction to supervised learning algorithms

Deep dive into supervised learning algorithms

Introduction to unsupervised learning algorithms

Deep dive into unsupervised learning algorithms

Summary

References

Random Forest on iOS

Introduction to algorithms

Solving the problem using random forest in Core ML

Summary

Further reading

Summary

The ML Kit SDK

Understanding ML Kit

Creating a text recognition app using Firebase on-device APIs

Creating a text recognition app using Firebase on-cloud APIs

Face detection using ML Kit

Summary

Spam Message Detection

Understanding NLP

Understanding linear SVM algorithm

Solving the problem using linear SVM in Core ML

Summary

Fritz

Introduction to Fritz

Hand-on samples using Fritz

Summary

Neural Networks on Mobile

Introduction to neural networks

Image recognition solution

Creating a TensorFlow image recognition model

Handwritten digit recognition solution

Introduction to Keras

Installing Keras

Solving the problem

Summary

Mobile Application Using Google Vision

Features of Google Cloud Vision

Sample mobile application using Google Cloud Vision

Summary

The Future of ML on Mobile Applications

Key ML mobile applications

Key innovation areas

Opportunities for stakeholders

Summary

Question and Answers

FAQs

References

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Fritz

We have gone through mobile machine learning SDKs offered by Google—TensorFlow for mobile—and Apple—Core ML—in the previous chapters and got a good understanding of them. We looked at the basic architecture of those products, the key features they offer, and also tried a few tasks/programs using those SDKs. Based on what we have explored on the mobile machine learning frameworks and tools so far, we will be able to identify a few gaps that make it difficult to carry out mobile machine learning deployments and subsequent maintenance and support of those deployments. Let me list a few for you:

Once we create the machine learning model and import it into the Android or iOS application, if there is any change that needs to be done to the model that was imported into the mobile application, how do you think this change will be implemented...