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

How decision tree learning works

Decision tree learning is a supervised, non-parametric algorithm used for classification and regression.

Building a tree automatically from data

The Twenty Questions game is a traditional game where one of the players is the answerer who chooses an object (or a famous person in some variants), not revealing what it is to the other participants. All the other players are trying to guess what the object is by asking questions like Can I eat this? or Is it a human? where answers can only be yes or no.

If you have never heard about this game, refer to Wikipedia:

This is essentially a tree learning algorithm. To win in a game, you should pose such questions that discriminate the most; for example, the question, Is it alive? in the beginning of the game is clearly better than Is it a cucumber?. This ability to dissect the hypothesis space in an optimal way is formalized in the notion of information gain criterion.