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

Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML
Machine Learning Projects for Mobile Applications: Build Android and iOS applications using TensorFlow Lite and Core ML by Karthikeyan NG
English | 2018 | ISBN: 1788994590 | 246 Pages | True PDF, EPUB | 368 MB

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