Machine Learning for Mobile

coverpage

Title Page

Copyright and Credits

Machine Learning for Mobile

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Contributors

About the authors

About the reviewer

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Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

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Conventions used

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Introduction to Machine Learning on Mobile

Definition of machine learning

When is it appropriate to go for machine learning systems?

The machine learning process

Defining the machine learning problem

Preparing the data

Building the model

Selecting the right machine learning algorithm

Training the machine learning model

Testing the model

Evaluation of the model

Making predictions/Deploying in the field

Types of learning

Supervised learning

Unsupervised learning

Semi-supervised learning

Reinforcement learning

Challenges in machine learning

Why use machine learning on mobile devices?

Ways to implement machine learning in mobile applications

Utilizing machine learning service providers for a machine learning model

Ways to train the machine learning model

On a desktop (training in the cloud)

On a device

Ways to carry out the inference – making predictions

Inference on a server

Inference on a device

Popular mobile machine learning tools and SDKs

Skills needed to implement on-device machine learning

Summary

Supervised and Unsupervised Learning Algorithms

Introduction to supervised learning algorithms

Deep dive into supervised learning algorithms

Naive Bayes

Decision trees

Linear regression

Logistic regression

Support vector machines

Random forest

Introduction to unsupervised learning algorithms

Deep dive into unsupervised learning algorithms

Clustering algorithms

Clustering methods

Hierarchical agglomerative clustering methods

K-means clustering

Association rule learning algorithm

Summary

References

Random Forest on iOS

Introduction to algorithms

Decision tree

Advantages of the decision tree algorithm

Disadvantages of decision trees

Advantages of decision trees

Random forests

Solving the problem using random forest in Core ML

Dataset

Naming the dataset

Technical requirements

Creating the model file using scikit-learn

Converting the scikit model to the Core ML model

Creating an iOS mobile application using the Core ML model

Summary

Further reading

TensorFlow Mobile in Android

An introduction to TensorFlow

TensorFlow Lite components

Model-file format

Interpreter

Ops/Kernel

Interface to hardware acceleration

The architecture of a mobile machine learning application

Understanding the model concepts

Writing the mobile application using the TensorFlow model

Writing our first program

Creating and Saving the TF model

Freezing the graph

Optimizing the model file

Creating the Android app

Copying the TF Model