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
by: Anubhav Singh and Rimjhim Bhadani
Print Length 页数: 380 pages
Publisher finelybook 出版社: Packt Publishing (6 April 2020)
Language 语言: English
ISBN-10: 1789611210
ISBN-13: 9781789611212
Book Description
Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite,ML Kit,and Flutter
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
Table of Contents
Preface
Chapter 01: Introduction to Deep Learning for Mobile
Chapter 02: Mobile Vision-Face Detection Using On-Device
Models
Chapter 03: Chatbot Using Actions on Google
Chapter 04: Recognizing Plant Species
Chapter 05: Generating Live Captions from a Camera Feed
Chapter 06: Building an Artificial Intelligence Authentication System
Chapter 07: Speech/Multimedia Processing -Generating Music
Using Al
Chapter 08: Reinforced Neural Network-Based Chess Engine
Chapter 09: Building an Image Super-Resolution Application
Chapter 10: Road Ahead
Appendix
Other Books You May Enjoy
Index
