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

Introducing computer vision problems

In this book, we mentioned computer vision several times, but since this chapter is focused on this particular domain, we will look at it in more detail now. There are several practical tasks related to image and video processing, which are referred to as computer vision domain. While working on some computer vision task, it's important to know these names, to be able to find what you need in the vast ocean of computer vision publications:

  • Object recognition: The same as classification. Assigning labels to the images. This is a cat. Age estimation. Facial expression recognition.
  • Object localization: Finding frame of object in the image. The cat is in this frame.
  • Object detection: Finding frames of objects in the image. The cat is in this frame.
  • Semantic segmentation: Each point in the picture is assigned to one class. If the picture contains several cats, each cat's pixel would be assigned to the cat class.
  • Instance segmentation: Each point in the picture...