#### 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 Regression and Gradient Descent
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

## Implementing logistic regression in Swift

The most important differences of this implementation from multiple linear regression are the following:

• Normalization is required only for feature matrix x, and not for the target vector y, because the output has range (0, 1)
• The hypothesis is different
• The cost function looks different, but the cost gradient remains the same

Again, we'll need some accelerate functions:

`import Accelerate `

The logistic regression class definition looks similar to multiple linear regression:

```public class LogisticRegression {
public var weights: [Double]!

public init(normalization: Bool) {
self.normalization = normalization
}

private(set) var normalization: Bool
private(set) var xMeanVec = [Double]()
private(set) var xStdVec = [Double]() ```

### The prediction part of logistic regression

This is the code that implements hypotheses for one sample input and for a matrix of inputs:

```public func predict(xVec: [Double]) -> Double {
if normalization {
let input...```