Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By : Anubhav Singh, Bhadani

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Buy this Book

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

1 (1)

By: Anubhav Singh, Bhadani

Buy this Book

Overview of this book

Deep learning is rapidly becoming the most popular topic in the mobile app industry. This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach. You will cover a range of projects covering tasks such as mobile vision, facial recognition, smart artificial intelligence assistant, augmented reality, and more. With the help of eight projects, you will learn how to integrate deep learning processes into mobile platforms, iOS, and Android. This will help you to transform deep learning features into robust mobile apps efficiently. You’ll get hands-on experience of selecting the right deep learning architectures and optimizing mobile deep learning models while following an application oriented-approach to deep learning on native mobile apps. We will later cover various pre-trained and custom-built deep learning model-based APIs such as machine learning (ML) Kit through Firebase. Further on, the book will take you through examples of creating custom deep learning models with TensorFlow Lite. Each project will demonstrate how to integrate deep learning libraries into your mobile apps, right from preparing the model through to deployment. By the end of this book, you’ll have mastered the skills to build and deploy deep learning mobile applications on both iOS and Android.

Preface

Who this book is for

What this book covers

To get the most out of this book

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Introduction to Deep Learning for Mobile

Growth of AI-powered mobile devices

Understanding machine learning and deep learning

Introducing some common deep learning architectures

Introducing reinforcement learning and NLP

Methods of integrating AI on Android and iOS

Summary

Free Chapter

Mobile Vision - Face Detection Using On-Device Models

Technical requirements

Introduction to image processing

Developing a face detection application using Flutter

Summary

Chatbot Using Actions on Google

Technical requirements

Understanding the tools available for creating chatbots

Creating a Dialogflow account

Creating a Dialogflow agent

Understanding the Dialogflow Console

Creating your first action on Google

Creating Actions on a Google project

Implementing a Webhook

Deploying a webhook to Cloud Functions for Firebase

Creating an Action on Google release

Creating the UI for the conversational application

Integrating the Dialogflow agent

Adding audio interactions with the assistant

Summary

Recognizing Plant Species

Technical requirements

Introducing image classification

Understanding the project architecture

Introducing the Cloud Vision API

Configuring the Cloud Vision API for image recognition

Using an SDK/tools to build a model

Creating a custom TensorFlow Lite model for image recognition

Creating a Flutter application

Running image recognition

Summary

Generating Live Captions from a Camera Feed

Designing the project architecture

Understanding an image caption generator

Understanding the camera plugin

Creating a camera application

Generating image captions from the camera feed

Creating the material app

Summary

Building an Artificial Intelligence Authentication System

Technical requirements

A simple login application

Adding Firebase authentication

Understanding anomaly detection for authentication

A custom model for authenticating users

Implementing ReCaptcha for spam protection

Deploying the model in Flutter

Summary

Speech/Multimedia Processing - Generating Music Using AI

Designing the project's architecture

Understanding multimedia processing

Developing RNN-based models for music generation

Deploying an audio generation API on Android and iOS

Summary

Reinforced Neural Network-Based Chess Engine

Introduction to reinforcement learning

Reinforcement learning in mobile games

Exploring Google's DeepMind

Alpha Zero-like AI for Connect 4

Underlying project architecture

Developing a GCP-hosted REST API for the chess engine

Creating a simple chess UI on Android

Integrating the chess engine API with a UI

Summary

Building an Image Super-Resolution Application

Basic project architecture

Understanding GANs

Understanding how image super-resolution works

Creating a TensorFlow model for super-resolution

Building the UI for the application

Getting pictures from the device's local storage

Hosting a TensorFlow model on DigitalOcean

Integrating a hosted custom model on Flutter

Creating the Material app

Summary

Road Ahead

Understanding recent trends in DL on mobile applications

Exploring the latest developments in DL on mobile devices

Exploring current research areas for DL in mobile apps

Summary

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Appendix

Setting up a deep learning environment on Cloud VM

Installing Dart SDK

Installing Flutter SDK

Configuring Firebase

Setting up VS Code

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Integrating the chess engine API with a UI

The hosted chess server will be added to the application as an opponent player. The user will be the white side, while the server will be the black side. The game logic to be implemented here is very simple. The first move is given to the application user. When the user makes a move, they change the state of the chessboard from state X to state Y. The state of the board is represented by a FEN string. Also, they move a piece from a particular square to a particular square, which contributes to their move. When the user has completed a move, the FEN string for state X and their current move, which is obtained by concatenating the from and to squares, is sent to the server in the form of a POST request. The server, in return, responds with the next move from its side, which is then reflected on the UI.

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