Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter
暫譯: 使用 TensorFlow Lite、ML Kit 和 Flutter 的行動深度學習

Singh, Anubhav, Bhadani, Rimjhim

Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter

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商品描述

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.

商品描述(中文翻譯)

學習如何在使用 TensorFlow Lite、ML Kit 和 Flutter 建立的跨平台應用程式上部署有效的深度學習解決方案

主要特色


  • 通過涵蓋移動視覺、風格轉換、語音處理和多媒體處理的專案進行實作

  • 涵蓋有趣的移動深度學習解決方案

  • 通過每個專案建立您在訓練模型、性能調優、記憶體優化和部署神經網絡方面的信心

書籍描述深度學習正迅速成為業界最受歡迎的主題。本書以工業和應用為重點，介紹了當前流行的深度學習概念及其使用案例。您將涵蓋一系列專案，涉及移動視覺、人臉識別、智能 AI 助手、擴增實境等任務。

在 8 個專案的幫助下，您將學會將深度學習過程整合到移動平台 iOS 和 Android 中。這將幫助您有效地將深度學習功能轉化為穩健的移動應用程式。本書將讓您親自選擇合適的深度學習架構並優化移動深度學習模型，同時遵循以應用為導向的深度學習方法，針對原生移動應用程式進行學習。接下來，您將涵蓋各種基於預訓練和自定義構建的深度學習模型的 API，例如通過 Google Firebase 和 Core ML 的 ML Kit。此外，本書將通過使用 Python 的 TensorFlow Lite 幫助您創建自定義深度學習模型的範例。每個專案將展示如何將深度學習庫整合到您的移動應用程式中，從準備模型到部署的整個過程。

在本書結束時，您將具備在 iOS 和 Android 上構建和部署高級深度學習移動應用程式的技能。

您將學到的內容


  • 通過擴展 Google Assistant 的功能來創建您自己的自定義聊天機器人

  • 利用移動設備上可用的功能提高學習準確性

  • 使用影像處理執行視覺識別任務

  • 使用擴增實境為相機畫面生成標題

  • 驗證用戶並創建識別稀有和可疑用戶互動的機制

  • 基於深度強化學習創建一個棋類引擎

  • 探索推出生產就緒的深度學習 iOS 和 Android 應用程式所涉及的概念和方法

本書適合對象本書適合希望使用深度學習方法構建智能移動應用程式的數據科學家、深度學習和計算機視覺工程師以及自然語言處理工程師。如果您希望通過利用深度學習的潛力來改善移動應用程式的 UI，本書也將對您有所幫助。具備基本的神經網絡知識和 Python 編程經驗將有助於您開始閱讀本書。

作者簡介

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.

作者簡介(中文翻譯)

Anubhav Singh 是 The Code Foundation 的創辦人，這是一家專注於人工智慧的初創公司，致力於多媒體處理和自然語言處理，目標是讓人工智慧對每個人都能夠獲得。作為網路奧林匹克的國際排名持有者，他不斷為社群開發軟體，探索那些不常被人走過的領域。Anubhav 是 Venkat Panchapakesan 紀念獎學金的得主，也是 Intel 軟體創新者。Anubhav 喜歡分享他的學習經驗，並且是全國 Google 開發者社群的活躍演講者，經常指導學習者在機器學習的旅程中前行。

Rimjhim Bhadani 是一位開源愛好者。她一直相信以最低的成本讓資源對每個人都能夠獲得。她是行動應用程式開發的忠實粉絲，並開發了多個專案，旨在解決日常生活中的重大和小型挑戰。她曾是 Google Code-In 的 Android 導師，並且是 Google Summer of Code 的 Android 開發者。為了支持她服務社群的願景，她是六位被認可為 Google Venkat Panchapakesan 紀念獎學金的印度學生之一，也是2019年三位獲得 Grace Hopper 學生獎學金的印度學生之一。

目錄大綱

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

目錄大綱(中文翻譯)


  1. Introduction to Deep Learning for Mobile

  2. Mobile Vision : Face Detection using on-device models

  3. Chatbot using Actions on Google

  4. Recognizing Plant Species

  5. Live Captions Generation of Camera Feed

  6. Building Artificial Intelligence Authentication System

  7. Speech/Multimedia Processing: Generating music using AI

  8. Reinforced Neural Network based Chess Engine

  9. Building Image Super-Resolution Application

  10. Road Ahead

  11. Appendix

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