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

Summary


In this chapter, we built a deep learning CNN, and trained it using Keras to recognize facial expressions on photos. Then we ported it for the mobile application using Core ML. The model can work in real time. We've also become acquainted with the Apple Vision framework.

CNNs are powerful tools that can be applied for many computer vision tasks, as well as for time-series prediction, natural language processing, and others. They are built around the concept of convolution—a mathematical operation that can be used for defining many types of image transformations. CNNs learn convolution filters in the similar manner as usual neural networks learn weights using the same stochastic gradient descent. Convolution requires less computations than usual matrix multiplications, which is why they can be effectively used on mobile devices. Apart from convolutional layers, CNNs usually include other types of layers like pooling, fully-connected, nonlinearity, regularization, and so on. Over the...