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


In this chapter, we've become acquainted with artificial NNs and their main components. NNs are built from neurons that are usually organized in layers. A typical neuron performs a weighted sum of inputs and then applies a non-linear activation function on it to calculate its output. There are many different activation functions, but the most popular these days is ReLU and its modifications, due to their computational properties.

NNs are usually trained using the backpropagation algorithm, built on top of stochastic gradient descent. Feed-forward NNs with several layers are also known as multilayer perceptrons. MLPs can be used for classification tasks.

In the next chapter, we'll continue to discuss NNs, but this time we'll focus on convolution NNs, which are especially popular in the computer vision domain.