Reinforcement learning has gained popularity among developers who wish to build game-playing AIs for various reasons – to simply check the capabilities of the AI, to build a training agent that helps professionals improve their game, and so on. From a researcher's point of view, games offer the best testing environment for reinforcement learning agents that can make decisions based on experience and learn to survive/achieve in any given environment. This is due to the fact that games can be designed with simple and precise rules, where the reaction of the environment to a certain action can be accurately predicted. This makes it easier to evaluate the performance of the reinforcement learning agents, and thereby facilitate a good training ground for the AI. With the breakthroughs in game-playing AIs taken into consideration, it...
Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter
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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter
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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)
Preface
Introduction to Deep Learning for Mobile
Free Chapter
Mobile Vision - Face Detection Using On-Device Models
Chatbot Using Actions on Google
Recognizing Plant Species
Generating Live Captions from a Camera Feed
Building an Artificial Intelligence Authentication System
Speech/Multimedia Processing - Generating Music Using AI
Reinforced Neural Network-Based Chess Engine
Building an Image Super-Resolution Application
Road Ahead
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