#### 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.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Getting Started with Machine Learning
Classification – Decision Tree Learning
K-Nearest Neighbors Classifier
K-Means Clustering
Association Rule Learning
Linear Classifier and Logistic Regression
Neural Networks
Convolutional Neural Networks
Natural Language Processing
Machine Learning Libraries
Optimizing Neural Networks for Mobile Devices
Best Practices
Index

## K-means++

An improved algorithm was proposed in 2007. K-means++ addresses the problem of suboptimal clustering by introducing an additional step for a good centroids initialization.

An improved algorithm of initial centers selection looks like this:

1. Select randomly any data point to be the first center
2. For all other data points, calculate the distance to the first center d(x)
3. Sample the next center from the weighted probability distribution, where the probability of each data point to become a next center is proportional to the square of distance d(x)2
4. Until k centers are chosen, repeat step 2 and step 3
5. Proceed with the standard k-means algorithm

In Swift, it looks like this:

```internal mutating func chooseCentroids() {
let n = data.count

var minDistances = [Double](repeating: Double.infinity, count: n)
var centerIndices = [Int]() ```

`clusterID` is an integer identifier of a cluster: the first cluster has identifier zero, the second has one, and so on:

```for clusterID in 0 ..< k {
var pointIndex...```