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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

Time to practice

In the following sections, we'll dive into machine learning practice, to get a feeling of what it looks like. Just like in a theater play, in machine learning you have a list of characters and a list of acts.

Two main characters are:

  • Dataset
  • Model

Three main acts are:

  • Dataset preparation
  • Model training
  • Model evaluation

We'll go through all these acts, and by the end of the chapter we'll have our first trained model. First, we need to define a problem, and then we can start coding a prototype in Python. Our destination point is a working model in Swift. Don't take the problem itself too seriously, though, because as the first exercise, we're going to solve a fictional problem.

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