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


For developing applications that can understand voice or text input, we use techniques from the natural language processing domain. We have just seen several widely used ways to preprocess texts: tokenization, stop words removal, stemming, lemmatization, POS tagging, and named entity recognition.

Word embedding algorithms, and mainly Word2Vec, draw inspiration from the distributive semantics hypothesis, which states that the meaning of the word is defined by its context. Using an autoencoder-like neural network, we learn fixed-size vectors for each word in a text corpus. Effectively, this neural network captures the context of the word and encodes it in the corresponding vector. Then, using linear algebra operations with those vectors, we can discover different interesting relationships between words. For example, it allows us to find semantically close words (cosine similarity between vectors).

In the next section of the book, we are going to dig deeper into some practical questions...