Book Image

Machine Learning for Mobile

By : Revathi Gopalakrishnan, Avinash Venkateswarlu
Book Image

Machine Learning for Mobile

By: Revathi Gopalakrishnan, Avinash Venkateswarlu

Overview of this book

Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Question and Answers
Index

Deep dive into supervised learning algorithms


Assume there are predictor attributes, x1, x2, .... xn, and also an objective attribute, y, for a given dataset. Then, the supervised learning is themachine learning task of finding the prediction function that takes as input both the predictor attributes and the objective attribute from this dataset, and is capable of mapping the predictive attributes to the objective attribute for even unseen data currently not in the training dataset with minimal error.

The data in the dataset used for arriving at the prediction function is called the training data and it consists of a set of training examples where each example consists of an input object, x (typically a vector), and a desired output value, Y. A supervised learning algorithm analyzes the training data and produces an inferred function that maps the input to output and could also be used for mapping new, unseen example data:

Y = f(X) + error

The whole category of algorithms is called supervised...