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

Low-level subroutine libraries

Some libraries are not doing machine learning on their own, but provide important low-level primitives for it. An example of such a library is Apple BNNS. It is a part of the Accelerate framework which provides highly optimized subroutines for convolutional neural networks. We'll discuss it in much detail in Chapter 10, Natural Language Processing. In the following section, we'll list some third-party libraries of its kind.


Eigen is a C++ template library implementing linear algebra primitives and related algorithms. It is under the LGPL3+ license. Many popular computationally-heavy projects (TensorFlow, for instance) rely on it for matrix and vector operations.


Official site:


fmincg-c conjugates gradient implementation in C. It uses OpenCL to process algorithms faster. There are some examples written in Python.


GitHub repository:


Audio feature extraction. The IntuneFeatures...