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

; Rimjhim Bhadani

Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter

Key Features

Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing
Cover interesting deep learning solutions for mobile
Build your confidence in training models, performance tuning, memory optimization, and neural network deployment through every project

Book DescriptionDeep learning is rapidly becoming the most popular topic in the mobile app industry. Les mer
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Paperback
Legg i
Paperback
Legg i
Vår pris: 466,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter

Key Features

Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing
Cover interesting deep learning solutions for mobile
Build your confidence in training models, performance tuning, memory optimization, and neural network deployment through every project

Book DescriptionDeep 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.

What you will learn

Create your own customized chatbot by extending the functionality of Google Assistant
Improve learning accuracy with the help of features available on mobile devices
Perform visual recognition tasks using image processing
Use augmented reality to generate captions for a camera feed
Authenticate users and create a mechanism to identify rare and suspicious user interactions
Develop a chess engine based on deep reinforcement learning
Explore the concepts and methods involved in rolling out production-ready deep learning iOS and Android applications

Who this book is forThis book is for data scientists, deep learning and computer vision engineers, and natural language processing (NLP) engineers who want to build smart mobile apps using deep learning methods. You will also find this book useful if you want to improve your mobile app's user interface (UI) by harnessing the potential of deep learning. Basic knowledge of neural networks and coding experience in Python will be beneficial to get started with this book.

Fakta

Innholdsfortegnelse

Table of Contents

Introduction to Deep Learning for Mobile
Mobile Vision : Face Detection using on-device models
Chatbot using Actions on Google
Recognizing Plant Species
Live Captions Generation of Camera Feed
Building Artificial Intelligence Authentication System
Speech/Multimedia Processing: Generating music using AI
Reinforced Neural Network based Chess Engine
Building Image Super-Resolution Application
Road Ahead
Appendix

Om forfatteren

Anubhav Singh is the Founder of The Code Foundation, an AI-focused startup which works on multimedia processing and natural language processing, with a goal of making AI accessible to everyone. An International Rank holder in the Cyber Olympiad, he's continuously developing software for the community in domains with roads less walked by. Anubhav is a Venkat Panchapakesan Memorial Scholarship awardee and an Intel Software Innovator. Anubhav loves talking about his learnings and is an active community speaker for Google Developer Groups all over the country and can o