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)

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...