Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By : Anubhav Singh, Rimjhim Bhadani
Book Image

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

By: Anubhav Singh, Rimjhim Bhadani

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.
Table of Contents (13 chapters)

Introduction to image processing

In this chapter, we shall be detecting faces in images. In the context of artificial intelligence, the action of processing an image for the purpose of extracting information about the visual content of that image is called image processing.

Image processing is an emerging field, thanks to the surge in the number of better AI-powered cameras, medical imagery-based machine learning, self-driving vehicles, analysis of people's emotions from images, and many other applications.

Consider the use of image processing by a self-driving vehicle. The vehicle needs to make decisions in as close to real time as possible to ensure the best possible accident-free driving. A delay in the response of the AI model running the car could lead to catastrophic consequences. Several techniques and algorithms have been developed for fast and accurate...