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
Contributors
Preface
Index

Using neurons to build logical functions


Among other obscured parts of iOS and macOS SDK, there is one interesting library called SIMD. It is an interface for direct access to vector instructions and vector types, which are mapped directly to the vector unit in the CPU, without the need to write an assembly code. You can reference vector and matrix types as well as linear algebra operators defined in this header right from your Swift code, starting from 2.0 version.

Note

The universal approximation theorem states that a simple NN with one hidden layer can approximate a wide variety of continuous functions if proper weights are found. This is also commonly rephrased as NNs as universal function approximators. However, the theorem doesn't tell if it's possible to find such proper weights.

To get access to those goodies, you need to import simd in Swift files, or #include <simd/simd.h> in C/C++/Objective-C files. GPU also has SIMD units in it, so you can import SIMD into your metal shader...