Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML

Karthikeyan NG


Bring magic to your mobile apps using TensorFlow Lite and Core ML

Key Features

  • Explore machine learning using classification, analytics, and detection tasks.
  • Work with image, text and video datasets to delve into real-world tasks
  • Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite

Book Description

Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so.

The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google's ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN.

By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML.

What you will learn

  • Demystify the machine learning landscape on mobile
  • Age and gender detection using TensorFlow Lite and Core ML
  • Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning
  • Create a digit classifier using adversarial learning
  • Build a cross-platform application with face filters using OpenCV
  • Classify food using deep CNNs and TensorFlow Lite on iOS

Who this book is for

Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.

Table of Contents

  1. Mobile Landscapes in Machine Learning
  2. CNN Based Age and Gender Identification Using Core ML
  3. Applying Neural Style Transfer on Photos
  4. Deep Diving into the ML Kit with Firebase
  5. A Snapchat-Like AR Filter on Android
  6. Handwritten Digit Classifier Using Adversarial Learning
  7. Face-Swapping with Your Friends Using OpenCV
  8. Classifying Food Using Transfer Learning
  9. What's Next?


帶著 TensorFlow Lite 和 Core ML 為您的手機應用程式帶來魔力

- 探索使用分類、分析和偵測任務的機器學習。
- 使用圖像、文字和視頻數據集進行真實世界任務。
- 使用 Caffe、Core ML 和 TensorFlow Lite 為 Android 和 iOS 構建應用程式。


本書從手機應用程式的機器學習基礎概念和如何為進一步的任務做好準備開始。您將首先開發一個使用 Core ML 和 TensorFlow Lite 進行年齡和性別分類的應用程式。您將探索神經風格轉換並熟悉深度卷積神經網絡的工作原理。我們還將更詳細地研究 Google 的 Firebase SDK 的 ML Kit,用於手機應用程式。您將學習如何在手機上檢測手寫文本。您還將學習如何通過使用面部特徵和 OpenCV 創建自己的 Snapchat 過濾器。您將學習如何在手機上訓練自己的食物分類模型;所有這些都將借助深度學習技術完成。最後,您將使用 TensorFlow Lite 和 RCNN 在手機和雲端上構建圖像分類器,比較其性能並分析結果。

通過閱讀本書,您不僅將掌握機器學習的概念,還將學習如何使用 TensorFlow Lite、Caffe2 和 Core ML 在手機上解決構建強大應用程式時遇到的問題。

- 解密手機上的機器學習領域。
- 使用 TensorFlow Lite 和 Core ML 進行年齡和性別檢測。
- 使用 Firebase 的 ML Kit 進行文本檢測、面部檢測和條碼掃描。
- 使用對抗學習創建數字分類器。
- 使用 OpenCV 在跨平台應用程式中使用面部過濾器。
- 在 iOS 上使用深度卷積神經網絡和 TensorFlow Lite 進行食物分類。

- 數據科學家、機器學習專家、深度學習或人工智慧愛好者,希望通過使用 TensorFlow Lite 和 CoreML 的實際示例來掌握機器學習和深度學習實施。具備 Python 編程語言的基本知識將是一個額外的優勢。

1. 手機應用程式中的機器學習環境
2. 使用 Core ML 的基於 CNN 的年齡和性別識別
3. 在照片上應用神經風格轉換
4. 深入研究 Firebase 的 ML Kit
5. Android 上的類似 Snapchat 的 AR 過濾器
6. 使用對抗學習進行手寫數字分類
7. 與朋友進行面部交換使用 OpenCV
8. 使用遷移學習進行食物分類
9. 下一步該怎麼做?