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 the Cloud Vision API

The Cloud Vision API is a popular API from the GCP suite. It has been a benchmark service for building applications using computer vision. Briefly, computer vision is the ability of computers to recognize entities in an image, ranging from human faces to roads and vehicles for autonomous driving tasks. Furthermore, computer vision can be used to automate tasks that are performed by the human visual system—such as counting the number of moving vehicles on a road, and observing changes in the physical environment. Computer vision has found a wide application in the following domains:

  • Tagging of recognized faces on social media platforms
  • Extracting text from images
  • Recognizing objects from images 
  • Autonomous driving vehicles 
  • Medical imagery-based predictions
  • Reverse image search
  • Landmark detection
  • Celebrity recognition ...