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

Common NLP approaches and subtasks

Most programmers are familiar with the simplest way of processing natural language: regular expressions. There are many regular expression implementations for different programming languages ​​that differ in small details. Because of these details, the same regular expression on various platforms can produce different results or not work at all. The two most popular standards are POSIX and Perl. The Foundation framework, however, contains its own version of regular expressions, based on the ICU C++ library. It is an extension of the POSIX standard for Unicode strings.

Why are we even talking about regular expressions here? Regular expressions are a great example of what NLP specialists call heuristics—manually written rules, ad hoc solutions, and describing a complex structure in such a way that all exceptions and variations are taken into account. Sophisticated heuristics require deep domain expertise to build. Only when we are not able to capture all the...