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)

Road Ahead

The most important part of a journey is knowing where to go next once it ends. We have covered some unique and powerful deep learning (DL) applications related to Flutter apps so far in this series of projects, but it is important for you to know where you can find more such projects, inspiration, and knowledge to build your own cool projects. In this chapter, we shall briefly cover the most popular applications using DL on mobile apps today, the current trends, and what is expected to come in this field in the future. 

In this chapter, we will cover the following topics:

  • Understanding recent trends in DL on mobile applications
  • Exploring the latest developments in DL on mobile devices
  • Exploring current research areas for DL in mobile apps

Let's begin by studying some of the trends in the world of DL mobile apps.