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

Prototyping the first machine learning app

Usually, before implementing a machine learning application for mobile devices, you want to do a quick and dirty prototype just to check your ideas. This allows to save a lot of time when you realize that the model you initially thought works perfectly for your problem, in reality doesn't. The quickest way to do a prototype is to use Python or R tools listed in the previous section.

Python is a general-purpose programming language with rich infrastructure and vibrant community. Its syntax is similar in many ways to Swift's one. Throughout this book, we'll use it for prototyping, and Swift for actual development.

When you have tested your ideas and a model prototype works as you expect, you can start thinking about how to port it to an iOS. You have several options here:

Inference-only options:

  • Check the Core ML, and a list of the Python libraries it supports. Maybe, you will be able to export your model in Core ML format, and run it on a device.
  • Write...