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)

Introducing image classification

Image classification is a major application domain for artificial intelligence (AI) in the modern day. We can find instances of image classification in a large number of places all around us, such as face unlocking for mobile phones, object recognition, optical character recognition, tagging of people in photos, and several others. While these task seems pretty simple when you think of it from a human's perspective, it is not as simple when it comes to computers. Firstly, the system has to recognize objects or people from an image and draw a bounding box around it/them and proceed to classification. Both these steps are compute-intensive and hard to perform for machines. 

There are several challenges in image processing that researchers are trying to overcome every day, such as face recognition for people with glasses on or a newly grown...