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)

Hosting a TensorFlow model on DigitalOcean

DigitalOcean is an amazing, low-cost cloud solutions platform that is very easy to get started with and offers nearly everything that an app developer might need for powering the backend of their app out of the box. The interface is very simple to use, and DigitalOcean boasts some of the most extensive documentation around getting started with setting up different types of application servers on the cloud.

In this project, we shall be using DigitalOcean's Droplet to deploy our super-resolution API. A Droplet in DigitalOcean is simply a virtual machine that usually runs on a shared hardware space. 

First, we'll create the file in the project directory and add the code required for the server to work.

Creating a Flask...