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 10. Natural Language Processing

Language is an integral part of our daily life and a natural way of conveying ideas from person to person. But as easy it is for us to understand our native language, it is just as difficult for computers to process it. The internet changed the science of language forever because it allowed collecting huge volumes of text and audio records. The field of knowledge that arose at the intersection of linguistics, computer science, and machine learning was called natural language processing (NLP).

In this chapter, we will get acquainted with the basic concepts and applications of NLP, relevant in the context of mobile development. We will talk about the powerful tools provided by iOS and the macOS SDK for language processing. We also will learn about the theory of distributional semantics and vector representations of words as its embodiment. They will allow us to express the meaning of sentences in the computer's favorite format—in the form of numbers....