Book Image

Machine Learning Projects for Mobile Applications

By : Karthikeyan NG
Book Image

Machine Learning Projects for Mobile Applications

By: Karthikeyan NG

Overview of this book

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.
Table of Contents (16 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

ML Kit basics


Of course, we can always do all the ML-based implementations without the help of Firebase. However, there are a few reasons why not everyone will be able to do this. The reason for this could be one of the following:

  • A very good mobile application developer may not be good at building an ML model. Building an ML model definitely takes time. This may vary on a case-by-case basis. 
  • Finding the right set of data models that will solve your use case will be a very difficult problem. Let's say you want to detect age and gender classification on an Asian person's face. In this case, the existing models that are available may not be accurate enough for your use case. 
  • Hosting your own model will be costlier and will require extra care on the server side of the application. 

The ML Kit is a combination of Google Cloud Vision API, Mobile Vision, and TensorFlow Lite models on a local device:

Basic feature set

ML Kit comes with a ready-to-use code base for common use cases such as detecting...