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

Transfer learning


Transfer learning is one of the popular approach in deep learning where a model developed for one task is reused for another model on a different task. Here pre-trained models are used as a first step on computer vision based tasks or natural language processing (NLP) based tasks provided we have very limited computational resources and time. 

In a typical computer vision based problem, neural networks try to detect edges in their initial level layers, shapes in the middle level layers and more specific features in the final level layers. With transfer learning, we will use the initial and middle level layers and only re-train the final level layers. 

For example, if we have a model trained to recognize an apple from the input image, it will be reused to detect water bottles. In the initial layers, the model has been trained to recognize objects so we will retrain only the final level layers. In that way, our model will learn what will differentiate water bottle from other...