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

Using instance-based models for classification and clustering

Instance-based machine learning algorithms are usually easy to understand as they have some geometrical intuition behind them. They can be used to perform different kinds of tasks, including classification, regression, clustering, and anomaly detection.

It's easy to confuse classification and clustering at first. Just to remind you, classification is one of the many types of supervised learning. The task is to predict some discrete label from the set of features (Figure 3.4, left pane). Technically, classification goes in two types: binary (check yes or no), and multiclass (yes/no/maybe/I don't know/can you repeat the question?). But in practice, you can always build a multiclass classifier from several binary classifiers.

On the other hand, clustering is the task of unsupervised learning. This means that, unlike classification, it knows nothing about data labels, and works out clusters of similar samples in your data on its own...