Anubhav Singh - 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
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- Book: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
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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
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. Youll 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, youll 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
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 apps 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- Introduction to Deep Learning for Mobile
- Mobile Vision : Face Detection using on-device models
- Chatbot using Actions on Google
- Recognizing Plant Species
- Live Captions Generation of Camera Feed
- Building Artificial Intelligence Authentication System
- Speech/Multimedia Processing: Generating music using AI
- Reinforced Neural Network based Chess Engine
- Building Image Super-Resolution Application
- Road Ahead
- Appendix
Anubhav Singh: author's other books
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Copyright 2020 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 authors, 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: Pravin Dhandhre
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First published: April 2020
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ISBN 978-1-78961-121-2
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Anubhav Singh is the founder of The Code Foundation, an AI-focused start-up that works on multimedia processing and natural language processing, with the 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 that don't get a lot of attention. Anubhav is a Venkat Panchapakesan Memorial Scholarship awardee and an Intel Software Innovator. Anubhav loves talking about what he's learned 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 a lover of open source. She has always believed in making development resources accessible to everyone at minimal costs. She is a big fan of mobile application development and has developed a number of projects, most of which aim to solve major and minor daily life challenges. She has been an Android mentor at Google Code-in and an Android developer for Google Summer of Code. Supporting her vision to serve the community, she is one of six Indian students to be recognized as a Google Venkat Panchapakesan Memorial Scholar and one of three Indian students to be awarded the Grace Hopper Student Scholarship in 2019.
Subhash Shah is an experienced solutions architect. With 14 years of experience in software development, he now works as an independent technical consultant. He is an advocate of open source development and its utilization in solving critical business problems. His interests include microservices architecture, enterprise solutions, machine learning, integrations, and databases. He is an admirer of quality code and test-driven development (TDD). His technical skills include translating business requirements into scalable architecture and designing sustainable solutions. He is a co-author of Hands-On High Performance with Spring 5, Hands-On AI for Banking, and MySQL 8 Administrators Guide, all from Packt Publishing. He has also been a technical reviewer for other books.
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.
Deep learning is rapidly becoming the most popular topic in the 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 AI assistants, and augmented reality.
With the help of eight projects, you will learn to integrate deep learning processes into the iOS and Android mobile platforms. This will help you to transform deep learning features into robust mobile apps efficiently. This book gets you hands-on with 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 pretrained and custom-built deep learning model-based APIs, such as the ML Kit through Google Firebase. Further on, the book will take you through examples of creating custom deep learning models with the help of TensorFlow Lite using Python. 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 the skills to build and deploy advanced deep learning mobile applications on both iOS and Android.
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