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

Word2Vec in Gensim

There is no point in running Word2Vec on an iOS device: in the app, we need only the vectors it generates. For running Word2Vec, we will use the Python NLP package gensim. This library is popular for topic modeling and contains a fast Word2Vec implementation with a nice API. We don't want to load large corpuses of text on a mobile phone and don't want to train Word2vec on the iOS device, so we will learn a vector representation using the Gensim Python library. Then, we will do some preprocessing (remove everything except nouns) and plug this database into our iOS application:

In [39]: 
import gensim 
In [40]: 
def trim_rule(word, count, min_count): 
    if word not in words_to_keep or word in stop_words: 
        return gensim.utils.RULE_DISCARD 
        return gensim.utils.RULE_DEFAULT 
In [41]: 
model = gensim.models.Word2Vec(sentences_to_train_on, min_count=15, trim_rule=trim_rule)