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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

You're reading from   Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter Build scalable real-world projects to implement end-to-end neural networks on Android and iOS

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789611212
Length 380 pages
Edition 1st Edition
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Authors (2):
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Rimjhim Bhadani Rimjhim Bhadani
Author Profile Icon Rimjhim Bhadani
Rimjhim Bhadani
Anubhav Singh Anubhav Singh
Author Profile Icon Anubhav Singh
Anubhav Singh
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Deep Learning for Mobile 2. Mobile Vision - Face Detection Using On-Device Models FREE CHAPTER 3. Chatbot Using Actions on Google 4. Recognizing Plant Species 5. Generating Live Captions from a Camera Feed 6. Building an Artificial Intelligence Authentication System 7. Speech/Multimedia Processing - Generating Music Using AI 8. Reinforced Neural Network-Based Chess Engine 9. Building an Image Super-Resolution Application 10. Road Ahead 11. Other Books You May Enjoy Appendix

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 flask_app.py file in the project directory and add the code required for the server to work.

Creating a Flask...

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