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

Chapter 3. K-Nearest Neighbors Classifier

This chapter is devoted to an important class of machine learning algorithms, known as instance-based models. The name comes from the fact that they are built around the notion of similarity between instances (distance) and the geometrical intuition behind it. As a practical application of our newly learned skills, we will build an app that recognizes types of user movements based on the data from motion sensors and learns completely on device (no Python this time).

The algorithms that we are discussing and implementing in this chapter are k-nearest neighbors (KNN) and dynamic time warping (DTW).

In this chapter, we will cover the following topics:

  • Choosing a distance metric—Euclidean, edit distance, taxicab, and DTW
  • Building a KNN multiclass classifier
  • Geometrical intuition behind machine learning models
  • Reasoning in high-dimensional spaces
  • Choosing hyperparameters