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

Building the network


Individual neurons can be organized in a network (see Figure 8.4), usually by joining several neurons in parallel in a layer and then stacking layers on top of each other. Such a network is known as a feed-forward NN or a multilayer perceptron (MLP). The first layer is an input layer, the last layer is an output layer, and all inner layers are known as hidden layers. If each neuron of one layer is connected to the all neurons in the next layer, such a network is called a fully-connected NN.

A fully-connected feed-forward multilayer perceptron with one type of activation (usually sigmoid) is a traditional (canonical) type of NN. It is mostly used for classification purposes. In the following chapters, we will discuss other types of NNs, but in this chapter we will stick to the MLP:

Figure 8.4: Fully-connected feed-forward NN with five layers