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

Vector space properties

"The Hatter opened his eyes very wide on hearing this; but all he SAID was, 'Why is a raven like a writing-desk?' 'Come, we shall have some fun now!' thought Alice. 'I'm glad they've begun asking riddles. - I believe I can guess that,' she added aloud. 'Do you mean that you think you can find out the answer to it?' said the March Hare."

– Lewis CarrollAlice in a Wonderland

Why is a raven like a writing desk? With the help of distributive semantic and vector word representations, finally we can help Alice to solve Hatter's riddle (in a mathematically precise way):

In [42]: 
model.most_similar('house', topn=5) 
[(u'camp', 0.8188982009887695), 
 (u'cabin', 0.8176383972167969), 
 (u'town', 0.7998955845832825), 
 (u'room', 0.7963996529579163), 
 (u'street', 0.7951667308807373)] 
In [43]: 
model.most_similar('America', topn=5) 
[(u'India', 0.8678370714187622), 
 (u'Europe', 0.8501001596450806), 
 (u'number', 0.8464810848236084), 
 (u'member', 0.8352445363998413...