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
Contributors
Preface
Index

Distributional semantics hypothesis


It’s difficult to say what it means “to understand meaning of a text”, but everyone will say that people can do this, and computers do not. Natural language understanding is one of the tough problems in Artificial Intelligence. How to capture the semantics of the sentence

Traditionally there were two opposite approaches to the problem. The first one goes like this: start from the definitions of separate words, hard-code the relations between them, and write down the sentence structures. If you are persistent enough, hopefully you will end up with a complex model that will incorporate enough expert knowledge to parse some natural questions and produce meaningful answers. And then, you'll find out that for a new language, you need to start everything over.

That's why many researchers turned to the opposite approach: statistical methods. Here, we start from a big amount of textual data and allow the computer to figure out the meaning of the text. The hypothesis...