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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

Developing a GCP-hosted REST API for the chess engine

Now that we have seen how we will be moving ahead with this project, we also need to discuss how we're going to map the game of Connect 4 to chess and deploy a chess RL engine as an API. 

You can find the files we've created for this chess engine at https://github.com/PacktPublishing/Mobile-Deep-Learning-Projects/tree/master/Chapter8/chess. Let's quickly understand some of the most important files before we map these files with those in the Connect 4 project: 

  • src/chess_zero/agent/:
  • player_chess.py: This file describes the ChessPlayer class, which holds information about the players playing the game at any point in time. It provides wrappers for the methods associated with searching for new moves using the Monte Carlo tree search, changing the player state, and other functions required during...
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