Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter

Key Features

• Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing
• Cover interesting deep learning solutions for mobile
• Build your confidence in training models, performance tuning, memory optimization, and neural network deployment through every project

Book Description

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.

What you will learn

• Create your own customized chatbot by extending the functionality of Google Assistant
• Improve learning accuracy with the help of features available on mobile devices
• Perform visual recognition tasks using image processing
• Use augmented reality to generate captions for a camera feed
• Authenticate users and create a mechanism to identify rare and suspicious user interactions
• Develop a chess engine based on deep reinforcement learning
• Explore the concepts and methods involved in rolling out production-ready deep learning iOS and Android applications

Who this book is for

This book is for data scientists, deep learning and computer vision engineers, and natural language processing (NLP) engineers who want to build smart mobile apps using deep learning methods. You will also find this book useful if you want to improve your mobile app's user interface (UI) by harnessing the potential of deep learning. Basic knowledge of neural networks and coding experience in Python will be beneficial to get started with this book.

Table of Contents

1. Introduction to Deep Learning for Mobile
2. Mobile Vision : Face Detection using on-device models
3. Chatbot using Actions on Google
4. Recognizing Plant Species
5. Live Captions Generation of Camera Feed
6. Building Artificial Intelligence Authentication System
7. Speech/Multimedia Processing: Generating music using AI
8. Reinforced Neural Network based Chess Engine
9. Building Image Super-Resolution Application
10. Road Ahead
11. Appendix

Anubhav Singh is the Founder of The Code Foundation, an AI-focused startup which works on multimedia processing and natural language processing, with a goal of making AI accessible to everyone. An International Rank holder in the Cyber Olympiad, he's continuously developing software for the community in domains with roads less walked by. Anubhav is a Venkat Panchapakesan Memorial Scholarship awardee and an Intel Software Innovator. Anubhav loves talking about his learnings and is an active community speaker for Google Developer Groups all over the country and can often be found guiding learners on their journey in machine learning.

Rimjhim Bhadani is an open source lover. She has always believed in making the resources accessible to everyone at a minimal cost. She is a big fan of Mobile Application Development and has developed a number of projects most which aim to solve major and minor daily life challenges. She has been an Android mentor in Google Code-In and an Android developer for Google Summer of Code. Supporting her vision to serve the community, she is one among the six Indian students to be recognized as Google Venkat Panchapakesan Memorial Scholar and one among the three Indian students to be awarded the Grace Hopper Student Scholarship in 2019.