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

Chapter 8. Classifying Food Using Transfer Learning

In this chapter, we are going to classify food items using transfer learning. For this, we have built our own TensorFlow-based machine learning (ML) model of some Indian food items that we will focus on. Millions of parameters are there with the modern recognition models. We need a lot of time and data to train a new model from scratch, as well as hundreds of Graphical Processing Units (GPUs) or Tensor Processing Units (TPUs) that run for hours. 

Transfer learning makes this task easier by using an existing model that is already trained and reusing it on a new model. In our example, we will use the feature extraction capabilities from the MobileNet model and train our own classifier on top of it. Even if we don't get 100% accuracy, this works best in a lot of cases and especially on a mobile phone, where we don't get heavy resources. We can easily train this model on a typical laptop for a few hours even without a GPU. This model is built...