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

Unsupervised learning

Unsupervised learning is a way of making hidden patterns in data visible:

  • Clustering finds groups or hierarchy of similar objects
  • Unsupervised anomaly detection finds outliers (weird samples)
  • Dimensionality reduction finds which details of data are the most important
  • Factor analysis reveals the latent variables that influence the behavior of the observed variables
  • Rule mining finds associations between different entities in the data

As usually, these tasks overlap pretty often, and many practical problems inhabit the neutral territory between supervised and unsupervised learning.

We will focus on clustering in this chapter and on rule mining in the next chapter. Others will remain mostly beyond the scope of this book, but in Chapter 10Natural Language Processing, we will nevertheless briefly discuss autoencoders; they can be used for both dimensionality reduction and anomaly detection.

Here are some examples of real-world tasks where clustering would be your tool of choice...