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    Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML
    Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML
    Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML
    Ebook445 pages2 hours

    Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML

    By Karthikeyan NG

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    About this ebook

    Bring magic to your mobile apps using TensorFlow Lite and Core ML




    Key Features



    • Explore machine learning using classification, analytics, and detection tasks.


    • Work with image, text and video datasets to delve into real-world tasks


    • Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite





    Book Description



    Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so.






    The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google's ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN.






    By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.




    What you will learn



    • Demystify the machine learning landscape on mobile


    • Age and gender detection using TensorFlow Lite and Core ML


    • Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning


    • Create a digit classifier using adversarial learning


    • Build a cross-platform application with face filters using OpenCV


    • Classify food using deep CNNs and TensorFlow Lite on iOS



    Who this book is for



    Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.

    LanguageEnglish
    PublisherPackt Publishing
    Release dateOct 31, 2018
    ISBN9781788998468
    Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML

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      Book preview

      Machine Learning Projects for Mobile Applications - Karthikeyan NG

      Machine Learning Projects for Mobile Applications

      Machine Learning Projects for Mobile Applications

      Build Android and iOS applications using TensorFlow Lite and Core ML

      Karthikeyan NG

      BIRMINGHAM - MUMBAI

      Machine Learning Projects for Mobile Applications

      Copyright © 2018 Packt Publishing

      All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

      Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

      Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

      Commissioning Editor: Sunith Shetty 

      Acquisition Editor: Dayne Castelino

      Content Development Editor: Rhea Henriques

      Technical Editor: Sayli Nikalje

      Copy Editor: Safis Editing

      Project Coordinator: Manthan Patel

      Proofreader: Safis Editing

      Indexer: Mariammal Chettiyar

      Graphics: Jisha Chirayil

      Production Coordinator: Aparna Bhagat

      First published: October 2018

      Production reference: 1311018

      Published by Packt Publishing Ltd.

      Livery Place

      35 Livery Street

      Birmingham

      B3 2PB, UK.

      ISBN 978-1-78899-459-0

      www.packtpub.com

      To my wife, Nanthana, for putting up with me during the course of this book. I know it must not have been easy.

      To my parents, for their constant support.

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      At www.packt.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks. 

      Contributors

      About the author

      Karthikeyan NG is the Head of Engineering and Technology at the Indian lifestyle and fashion retail brand. He served as a software engineer at Symantec Corporation and has worked with two US-based startups as an early employee and has built various products. He has 9+ years of experience in various scalable products using Web, Mobile, ML, AR, and VR technologies. He is an aspiring entrepreneur and technology evangelist. His interests lie in exploring new technologies and innovative ideas to resolve a problem. He has also bagged prizes from more than 15 hackathons, is a TEDx speaker and a speaker at technology conferences and meetups as well as guest lecturer at a Bengaluru University. When not at work, he is found trekking.

      I would like to thank Saurav Satpathy for helping me with the codes in one of the chapters. I would like to extend my gratitude to Varsha Shetty for presenting the idea of the book, and to Rhea Henriques for her tenacity. Thanks to Akshi, Tejas, Sayli, and the technical reviewer, Mayur, and the editorial team. I would also like to thank the open source community for making this book possible with the frameworks on both Android and iOS platforms.

      About the reviewer

      Mayur Ravindra Narkhede has a good blend of experience in data science and industrial domain. He is a researcher with a B.Tech in computer science and an M.Tech in CSE with a specialization in Artificial Intelligence.

      A data scientist whose core experience lies in building automated end-to-end solutions, he

      is proficient at applying technology, AI, ML, data mining, and design thinking to better

      understand and predict improvements in business functions and desirable requirements

      with growth profitability.

      He has worked on multiple advanced solutions, such as ML and predictive model

      development for the oil and gas industry, financial services, road traffic and transport, life

      sciences, and the big data platform for asset-intensive industries.

      Packt is searching for authors like you

      If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.

      Table of Contents

      Title Page

      Copyright and Credits

      Machine Learning Projects for Mobile Applications

      Dedication

      Packt Upsell

      Why subscribe?

      Packt.com

      Contributors

      About the author

      About the reviewer

      Packt is searching for authors like you

      Preface

      Who this book is for

      What this book covers

      To get the most out of this book

      Download the code

      Download the color images

      Conventions used

      Get in touch

      Reviews

      Mobile Landscapes in Machine Learning

      Machine learning basics

      Supervised learning

      Unsupervised learning

      Linear regression - supervised learning

      TensorFlow Lite and Core ML

      TensorFlow Lite

      Supported platforms

      TensorFlow Lite memory usage and performance 

      Hands-on with TensorFlow Lite 

      Converting SavedModel into TensorFlow Lite format

      Strategies

      TensorFlow Lite on Android

      Downloading the APK binary

      TensorFlow Lite on Android Studio

      Building the TensorFlow Lite demo app from the source

      Installing Bazel

      Installing using Homebrew

      Installing Android NDK and SDK

      TensorFlow Lite on iOS

      Prerequisites

      Building the iOS demo app

      Core ML

      Core ML model conversion

      Converting your own model into a Core ML model

      Core ML on an iOS app

      Summary

      CNN Based Age and Gender Identification Using Core ML

      Age, gender, and emotion prediction

      Age prediction

      Gender prediction

      Convolutional Neural Networks 

      Finding patterns

      Finding features from an image

      Pooling layer

      Rectified linear units

      Local response normalization layer

      Dropout layer

      Fully connected layer

      CNNs for age and gender prediction

      Architecture

      Training the network

      Initializing the dataset

      The implementation on iOS using Core ML

      Summary

      Applying Neural Style Transfer on Photos

      Artistic neural style transfer

      Background

      VGG network

      Layers in the VGG network

      Building the applications

      TensorFlow-to-Core ML conversion

      iOS application

      Android application

      Setting up the model

      Training your own model

      Building the application

      Setting up the camera and an image picker 

      Summary

      References

      Deep Diving into the ML Kit with Firebase

      ML Kit basics

      Basic feature set

      Building the application

      Adding Firebase to our application

      Face detection

      Face orientation tracking

      Landmarks

      Classification

      Implementing face detection

      Face detector configuration

      Running the face detector

      Step one: creating a FirebaseVisionImage from the input

      Using a bitmap

      From media.Image

      From a ByteBuffer

      From a ByteArray

      From a file

      Step two: creating an instance of FirebaseVisionFaceDetector object

      Step three: image detection

      Retrieving information from detected faces

      Barcode scanner

      Step one: creating a FirebaseVisionImage object

      From bitmap

      From media.Image

      From ByteBuffer

      From ByteArray

      From file

      Step two: creating a FirebaseVisionBarcodeDetector object

      Step three: barcode detection

      Text recognition

      On-device text recognition

      Detecting text on a device

      Cloud-based text recognition

      Configuring the detector

      Summary

      A Snapchat-Like AR Filter on Android

      MobileNet models

      Building the dataset

      Retraining of images 

      Model conversion from GraphDef to TFLite

      Gender model

      Emotion model

      Comparison of MobileNet versions

      Building the Android application

      References

      Questions

      Summary

      Handwritten Digit Classifier Using Adversarial Learning

      Generative Adversarial Networks

      Generative versus discriminative algorithms

      Steps in GAN

      Understanding the MNIST database

      Building the TensorFlow model

      Training the neural network

      Building the Android application

      FreeHandView for writing

      Digit classifier

      Summary

      Face-Swapping with Your Friends Using OpenCV

      Understanding face-swapping

      Steps in face-swapping

      Facial key point detection

      Identifying the convex hull

      Delaunay triangulation and Voronoi diagrams

      Affine warp triangles

      Seamless cloning

      Building the Android application

      Building a native face-swapper library

      Android.mk

      Application.mk

      Applying face-swapping logic

      Building the application

      Summary

      References

      Questions

      Classifying Food Using Transfer Learning

      Transfer learning

      Approaches in transfer learning

      Training our own TensorFlow model 

      Installing TensorFlow

      Training the images

      Retraining with own images

      Training steps parameter

      Architecture

      Distortions

      Hyperparameters

      Running the training script

      Model conversion

      Building the iOS application

      Summary

      What's Next?

      What you have learned so far

      Where to start when developing an ML application

      IBM Watson services

      Microsoft Azure Cognitive Services

      Amazon ML

      Google Cloud ML

      Building your own model

      Limitations of building your own model

      Personalized user experience

      Better search results

      Targeting the right user

      Summary

      Further reading

      Other Books You May Enjoy

      Leave a review - let other readers know what you think

      Preface

      Machine learning is a growing technique that focuses on the development of computer programs that can be changed or modified when exposed to new data. It has made significant advances that have enabled practical applications of machine learning (ML) and, by extension, the overall field of Artificial Intelligence (AI).

      This book presents the implementation of seven practical, real-world projects that will teach you how to leverage TensorFlow Lite and Core ML to perform efficient machine learning. We will be learning about the recent advancements in TensorFlow and its extensions, such as TensorFlow Lite, to design intelligent apps that learn from complex/large datasets. We will delve into advancements such as deep learning by building apps using deep neural network architecture such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), transfer learning, and much more. 

      By the end of this book, you will not only have mastered all the concepts of and learned how to implement machine learning and deep learning, but you will also have learned how to solve the problems and challenges faced while building powerful apps on mobile using TensorFlow Lite and Core ML.

      Who this book is for

      Machine Learning Projects for Mobile Applications is for you if you are a data scientist, ML expert, deep learning, or AI enthusiast who fancies mastering ML and deep learning implementation with practical examples using TensorFlow and Keras. Basic knowledge of Python programming language would be an added advantage.

      What this book covers

      Chapter 1, Mobile Landscapes in Machine Learning, makes us familiar with the basic ideas behind TensorFlow Lite and Core ML.

      Chapter 2, CNN Based Age and Gender Identification Using Core ML, teaches us how to build an iOS application to detect the age, gender, and emotion of a person from a camera feed or from the user's photo gallery using the existing data models that were built for the same purpose.

      Chapter 3, Applying Neural Style Transfer on Photos, teaches us how to build a complete iOS and Android application in which image transformations are applied to our own images in a fashion similar to the Instagram app.

      Chapter 4, Deep Diving into the ML Kit with Firebase, explores the Google Firebase-based ML Kit platform for mobile applications.

      Chapter 5, A Snapchat-Like AR Filter on Android, takes us on a journey where we will build an AR filter that is used on applications such as Snapchat and Instagram using TensorFlow Lite.

      Chapter 6, Handwritten Digit Classifier Using Adversarial Learning, explains how to build an Android application that identifies handwritten digits. 

      Chapter 7, Face-Swapping with Your Friends Using OpenCV, takes a close look at building an application where a face in an image is replaced by another face.

      Chapter 8, Classifying Food Using Transfer Learning, explains how to classify food items using transfer learning. 

      Chapter 9, What's Next?, gives us a glimpse into all the applications built throughout the book and their relevance in the future.

      To get the most out of this book

      If you have prior knowledge of building mobile apps, that will help greatly. If not, it is advisable to learn the basics of Java or Kotlin for Android, or Swift for iOS. 

      If you have basic knowledge of Python, that will help you build your own data model, but Python skill is not mandatory. 

      The applications in the book are built using a MacBook Pro. Most of the command-line operations are shown with the assumption that you have a bash shell installed on your machine. They may not work in a Windows development environment.

      Download the code

      You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

      You can download the code files by following these steps:

      Log in or register at www.packt.com.

      Select the SUPPORT tab.

      Click on Code Downloads & Errata.

      Enter the name of the book in the Search box and follow the onscreen instructions.

      Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

      WinRAR/7-Zip for Windows

      Zipeg/iZip/UnRarX for Mac

      7-Zip/PeaZip for Linux

      The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-Projects-for-Mobile-Applications. In case there's an update to the code, it will be updated on the existing GitHub repository.

      We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

      Download the color images

      We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781788994590_ColorImages.pdf.

      Conventions used

      There are a number of text conventions

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