Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter
內容描述
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 deploying neural networks through every project
Book DescriptionDeep learning is rapidly becoming the most popular topic in the 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 AI assistant, augmented reality, and more.
With the help of 8-projects, you will learn 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. This book gets you hands-on with 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. You will later cover various pre-trained and custom-built deep learning model-based APIs such as the ML Kit through Google Firebase and Core ML. Further on, the book will take you through examples of creating custom deep learning models with the help of TensorFlow Lite using Python. 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 the skills to build and deploy advanced 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
Create 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 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 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.
目錄大綱
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
作者介紹
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 often be found guiding learners on their journey in machine learning.
Rimjhim Bhadani is an open source lover. She has always believed in making the resources accessible to everyone at a minimal cost. She is a big fan of Mobile Application Development and has developed a number of projects most which aim to solve major and minor daily life challenges. She has been an Android mentor in Google Code-In and an Android developer for Google Summer of Code. Supporting her vision to serve the community, she is one among the six Indian students to be recognized as Google Venkat Panchapakesan Memorial Scholar and one among the three Indian students to be awarded the Grace Hopper Student Scholarship in 2019.
