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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

Creating a simple chess UI on Android

Now that we understand reinforcement learning and how to use it to develop a chess engine that can be deployed to GCP, let's create a Flutter application for the game. The application will have two players – the user and the server. The user is the person playing the game, while the server is the chess engine that we have hosted on GCP. First, the user makes a move. This move is recorded and is sent to the chess engine in the form of a POST request. The chess engine then responds with its own movement, which is then updated on the screen.

We will create a simple single-screen application with a chessboard placed at the center, as well as the chess pieces. The application would appear as follows:

The widget tree of the application will look as follows:

Let's start coding the application.

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