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

Implementing layers in Swift


There are at least three options to consider when you want to implement a NN in Swift:

  • Implement it in pure Swift (which may be useful mostly for the study purposes). A lot of implementations of different complexity and functionality can be found on the GitHub. It looks like every programmer at some stage of her/his life starts to write a NN library in her/his favourite programming language.
  • Implement it using low-level acceleration libraries—Metal Performance Shaders, or BNNS.
  • Implement it using some general-purpose NN framework—Keras, TensorFlow, PyTorch, and so on—and then convert it to Core ML format.

Note

The Metal Performance Shader library includes three types of activations for NNs: ReLU, sigmoid, and TanH (MPSCNNNeuronReLU, MPSCNNNeuronSigmoid, MPSCNNNeuronTanH). For more information refer to: https://developer.apple.com/reference/metalperformanceshaders.