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

K-means clustering

The name of this algorithm comes from the k clusters into which the samples are divided, and the fact that each cluster is grouped around some mean value, a centroid of a cluster. This centroid serves as a prototype of a class. Each data point belongs to the cluster which centroid is the closest.

The algorithm was invented in 1957 at Bell Labs.

In this algorithm, each data point belongs to only one cluster. As a result of this algorithm, we get the feature space partitioned into Voronoi cells.


Because of the k in its name, this algorithm is often confused with the KNN algorithm, but as we already have seen with k-fold cross-validation, not all ks are the same. You may wonder why machine learning people are so obsessed with this letter that they put it in every algorithm's name. I don't k-now.

Figure 4.1: Four different ways to cluster the same data using k-means algorithm. Bald black dots are centroids of clusters. The samples are from the classical Iris dataset, plotted...