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

Time to practice

In the following sections, we'll dive into machine learning practice, to get a feeling of what it looks like. Just like in a theater play, in machine learning you have a list of characters and a list of acts.

Two main characters are:

  • Dataset
  • Model

Three main acts are:

  • Dataset preparation
  • Model training
  • Model evaluation

We'll go through all these acts, and by the end of the chapter we'll have our first trained model. First, we need to define a problem, and then we can start coding a prototype in Python. Our destination point is a working model in Swift. Don't take the problem itself too seriously, though, because as the first exercise, we're going to solve a fictional problem.