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 is an efficient algorithm for word embeddings generation based on neural networks. It was originally described by Mikolov et al. in Distributed Representations of Words and Phrases and their Compositionality (2013). The original C implementation in the form of a command-line application is available at

Figure 10.4: Architecture of Word2Vec

Word2Vec is often referred to as an instance of deep learning, but the architecture is actually quite shallow: only three layers in depth. This misconception is likely related to its wide adoption for enhancing productivity of deep networks in NLP. The Word2Vec architecture is similar to an autoencoder. The input of the neural network is a sufficiently big text corpus, and the output is a list of vectors (arrays of numbers), one vector for each word in the corpus. The algorithm uses the context of each word to encode in those vectors information about co-occurrences of words. As a result, the...