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 Deep Learning for Mobile

In this chapter, we will explore the emerging avenues of deep learning on mobile devices. We will briefly discuss the basic concepts of machine learning and deep learning, and we'll introduce the various options available for integrating deep learning with Android and iOS. This chapter also introduces implementations of deep learning projects using native and cloud-based learning methodologies.

In this chapter, we will cover the following topics:

  • Growth of artificial intelligence (AI)-powered mobile devices
  • Understanding machine learning and deep learning
  • Introducing to some common deep learning architectures
  • Introducing to reinforcement learning and natural language processing (NLP)
  • Methods of integrating AI on Android and iOS