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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

Integrating the chess engine API with a UI

The hosted chess server will be added to the application as an opponent player. The user will be the white side, while the server will be the black side. The game logic to be implemented here is very simple. The first move is given to the application user. When the user makes a move, they change the state of the chessboard from state X to state Y. The state of the board is represented by a FEN string. Also, they move a piece from a particular square to a particular square, which contributes to their move. When the user has completed a move, the FEN string for state X and their current move, which is obtained by concatenating the from and to squares, is sent to the server in the form of a POST request. The server, in return, responds with the next move from its side, which is then reflected on the UI.

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