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

Mobile machine learning project life cycle

When developing a mobile machine learning product, you typically go through several stages:

  • Preparatory stage
  • Prototype creation
  • Porting to a mobile platform or deployment of the trained model
  • Production

Depending on your situation, your route may be shorter or longer; but usually, if you have skipped some stage, it just means that someone else did it for you. In the following explanation, we are omitting all the steps that are common to all kinds of mobile app projects and focusing only on the steps specific to machine learning.

Preparatory stage

This is the stage where you basically decide what you will do. There can be two possible outcomes for this stage: you have a plan on how to proceed, or you decide that you will not proceed:

Figure 13.1: Preparatory stage map

Formulate the problem

If you can solve your problem without machine learning, don't use it. If the task can be solved with traditional programming techniques, congratulations! You don't need...