Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

Machine learning for extra-terrestrial life explorers

Swift is undoubtedly the programming language of the future. In the nearest years, we're expecting to see Swift being employed to program-intelligent scout robots that will explore alien planets and life forms on them. These robots should be able to recognize and classify aliens they will encounter. Let's build a model to distinguish between two alien species using their characteristic features.

The biosphere of the distant planet consists mainly of two species: night predators rabbosauruses, and peaceful, herbivorous platyhogs (see the following diagram). Roboscouts are equipped with sensors to measure only three features of each individual: length (in meters), color, and fluffiness.

Figure 2.1: Objects of interest in our first machine learning task. Picture by Mykola Sosnovshchenko.


The full code of the Python part of this chapter can be found here: ML_Intro.ipynb.