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

Basic neural network subroutines (BNNS)


BNNS is a submodule of Accelerate, containing convolution NN primitives optimized for running inference on CPU. It was introduced in iOS 10 and macOS 10.12. Note that it contains only functions for inference, not for training.

The motivation behind this library was to provide unified API for common routines, such that app developers wouldn't need to re-implement convolutions and other primitives from scratch every time (which is hard, as we have seen already in the chapter on CNNs). In a typical CNN, most energy is spent in the convolution layers. Fully connected layers are more expensive computationally, but usually CNNs contain one or a few of them at the very end so convolutions still consume about 70% of energy. That's why it's important to have highly-optimized convolution layers. Unlike MPS, CNN is available on iOS, macOS, tvOS, and even watchOS. So, if you want to run deep learning on a TV set or your watch (just because you can), this is your...