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


In this chapter, we've discussed an important unsupervised learning task: clustering. The simplest clustering algorithm is k-means. It doesn't provide stable results and is computationally complex, but this can be improved using k-means++. The algorithm can be applied to any data for which Euclidean distance is a meaningful measure, but the best area to apply it is a signal quantization. For instance, we've used it for image segmentation. Many more clustering algorithms exist for different types of tasks.

In the next chapter, we're going to explore unsupervised learning more deeply. Specifically, we're going to talk about algorithms for finding association rules in data: association learning.