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Machine Learning with Swift

You're reading from   Machine Learning with Swift Artificial Intelligence for iOS

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Machine Learning FREE CHAPTER 2. Classification – Decision Tree Learning 3. K-Nearest Neighbors Classifier 4. K-Means Clustering 5. Association Rule Learning 6. Linear Regression and Gradient Descent 7. Linear Classifier and Logistic Regression 8. Neural Networks 9. Convolutional Neural Networks 10. Natural Language Processing 11. Machine Learning Libraries 12. Optimizing Neural Networks for Mobile Devices 13. Best Practices

Deploying CNN to iOS

You need to drag-and-drop the Core ML file generated in the previous section into your project to start working with the model.

Imports:

import Foundation
import Vision
import AVFoundation
import UIKit

At first, let's define some data structures. An enumeration for possible classification results:

enum FaceExpressions: String {
case angry = "angry"
case anxious = "anxious"
case neutral = "neutral"
case happy = "happy"
case sad = "sad"
}

An enum for errors of the classifier:

enum ClassifierError: Error {
case unableToResizeBuffer
case noResults
}

Classifier is a wrapper singleton for Core ML model:

class Classifier {
public static let shared = Classifier()

private let visionModel: VNCoreMLModel
var visionRequests = [VNRequest]()
var completion: ((_ label: [(FaceExpressions, Double)], _ error: Error?)->(...
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