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 learned about the main concepts in ML .

We discussed different definitions and subdomains of artificial intelligence, including ML . ML is the science and practice of extracting knowledge from data. We also explained the motivation behind ML . We had a brief overview of its application domains: digital signal processing, computer vision, and natural language processing.

We learned about the two core concepts in ML : the data, and the model. Your model is only as good as your data. A typical ML dataset consists of samples; each sample consists of features. There are many types of features and many techniques to extract useful information from the features. These techniques are known as feature engineering. For supervised learning tasks, dataset also includes label for each of the samples. We provided an overview of data collection and preprocessing.

Finally, we learned about three types of common ML tasks: supervised, unsupervised, and reinforcement learning. In the next chapter, we're going to build our first ML application.