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


In this chapter, we implemented a working machine learning solution for motion data classification and trained it end-to-end on a device. The simplest of the instance-based models is the nearest neighbors classifier. You can use it to classify any type of data, the only tricky thing is to choose a suitable distance metric. For feature vectors (points in n-dimensional space), many metrics have been invented, such as the Euclidean and Manhattan distances. For strings, editing distances are popular. For time series, we applied DTW.

The nearest neighbors method is a non-parametric model, which means that we can apply it without regard to statistical data distributions. Another advantage is that it is well suited for online learning and is easy to parallelize. Among the shortcomings is the curse of dimensionality and the algorithmic complexity of predictions (lazy learning).

In the next chapter, we're going to proceed with instance-based algorithms, this time focusing on the unsupervised...