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

MobileNet models


We use the MobileNet model to identify gender, while the AffectNet model is used to detect emotion. Facial key point detection is achieved using Google's Mobile Vision API. 

Neural networks and deep learning have sparked tremendous progress in the field of natural language processing (NLP) and computer vision. While many of the face, object, landmark, logo and text recognition technologies are provided for internet-connected devices, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of users, anytime, anywhere, regardless of internet connection. However, computer vision for on device and embedded applications face many challenges—models must run quickly with high accuracy in a resource-constrained environment making use of limited computation, power, and space. 

 

TensorFlow offers various pre-trained models, such as drag and drop models, in order to identify approximately 1,000 default objects...