Machine Learning Projects for Mobile Applications by Karthikeyan NG -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Machine Learning Projects for Mobile Applications

Dedication Packt Upsell

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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

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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

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