Machine Learning for Mobile by Gopalakrishnan, Revathi -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Machine Learning for Mobile

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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 Download the color images 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 Creating an activity

Summary

Regression Using Core ML in iOS

Introduction to regression

Linear regression

Dataset Dataset naming

Understanding the basics of Core ML Solving the problem using regression in Core ML

Technical requirements How to create the model file using scikit-learn Running and testing the model Importing the model into the iOS project Writing the iOS application Running the iOS application

Further reading Summary

The ML Kit SDK

Understanding ML Kit

ML Kit APIs

Text recognition Face detection Barcode scanning Image labeling Landmark recognition Custom model inference

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

Face detection concepts Sample solution for face detection using ML Kit Running the app

Summary

Spam Message Detection

Understanding NLP

Introducing NLP Text-preprocessing techniques

Removing noise Normalization Standardization

Feature engineering

Entity extraction Topic modeling Bag-of-words model Statistical Engineering TF–IDF TF Inverse Document Frequency (IDF) TF-IDF

Classifying/clustering the text

Understanding linear SVM algorithm Solving the problem using linear SVM in Core ML

About the data Technical requirements Creating the Model file using Scikit Learn Converting the scikit-learn model into the Core ML model Writing the iOS application

Summary

Fritz

Introduction to Fritz

Prebuilt ML models Ability to use custom models Model management

Hand-on samples using Fritz

Using the existing TensorFlow for mobile model in an Android application using Fritz

Registering with Fritz Uploading the model file (.pb or .tflite) Setting up Android and registering the app Adding Fritz's TFMobile library Adding dependencies to the project Registering the FritzJob service in your Android Manifest Replacing the TensorFlowInferenceInterface class with Fritz Interpreter Building and running the application Deploying a new version of your model

Creating an android application using fritz pre-built models

Adding dependencies to the project Registering the Fritz JobService in your Android Manifest Creating the app layout and components Coding the application

Using the existing Core ML model in an iOS application using Fritz

Registering with Fritz Creating a new project in Fritz Uploading the model file (.pb or .tflite) Creating an Xcode project Installing Fritz dependencies Adding code Building and running the iOS mobile application

Summary

Neural Networks on Mobile

Introduction to neural networks

Communication steps of a neuron The activation function Arrangement of neurons Types of neural networks

Image recognition solution Creating a TensorFlow image recognition model

What does TensorFlow do? Retraining the model

About bottlenecks

Converting the TensorFlow model into the Core ML model Writing the iOS mobile application

Handwritten digit recognition solution Introduction to Keras Installing Keras Solving the problem

Defining the problem statement Problem solution

Preparing the data Defining the model's architecture Compiling and fitting the model Converting the Keras model into the Core ML model Creating the iOS mobile application

Summary

Mobile Application Using Google Vision

Features of Google Cloud Vision Sample mobile application using Google Cloud Vision

How does label detection work? Prerequisites Preparations Understanding the Application Output

Summary

The Future of ML on Mobile Applications

Key ML mobile applications

Facebook Google Maps Snapchat Tinder Netflix Oval Money ImprompDo Dango Carat Uber GBoard

Key innovation areas

Personalization applications Healthcare Targeted promotions and marketing Visual and audio recognition E-commerce Finance management Gaming and entertainment Enterprise apps Real estate Agriculture Energy Mobile security

Opportunities for stakeholders

Hardware manufacturers Mobile operating system vendors Third-party mobile ML SDK providers ML mobile application developers

Summary

Question and Answers

FAQs

Data science

What is data science? Where is data science used? What is big data? What is data mining? Relationship between data science and big data What are artificial neural networks? What is AI? How are data science, AI, and machine learning interrelated?

Machine learning framework

Caffe2 scikit-learn TensorFlow Core ML

Mobile machine learning project implementation

What are the high-level important items to be considered before starting the project? What are the roles and skills required to implement a mobile machine learning project? What should you focus on when testing the mobile machine learning project? What is the help that the domain expert will provide to the machine learning project? What are the common pitfalls in machine learning projects?

Installation

Python Python dependencies Xcode

References

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