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

Feature standardization

An alternative approach is feature standardization:

Which of the two to use in your app is up to you, but be sure to use at least one of them.

As usual, we have an accelerate function for that:

func normalize(vec: [Double]) -> (normalizedVec: [Double], mean: Double, std: Double) { 
    let count = vec.count 
    var mean = 0.0 
    var std = 0.0 
    var normalizedVec = [Double](repeating: 0.0, count: count) 
    vDSP_normalizeD(UnsafePointer(vec), 1, &normalizedVec, 1, &mean, &std, UInt(count)) 
    return (normalizedVec, mean, std) 

Now we need to update the train method:

func train(xVec: [Double], yVec: [Double], learningRate: Double, maxSteps: Int) { 
    precondition(xVec.count == yVec.count) 
    precondition(maxSteps > 0) 
    if normalization { 
        let (normalizedXVec, xMean, xStd) = normalize(vec: xVec) 
        let (normalizedYVec, yMean, yStd) = normalize(vec: yVec) 
        // Save means and std-s for prediction phase.