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Machine Learning with Swift

You're reading from Machine Learning with Swift Artificial Intelligence for iOS

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781787121515

Length 378 pages

Edition 1st Edition

Languages

Swift

Tools

iOS

Concepts

Artificial Intelligence

Authors (3):

Jojo Moolayil

Oleksandr Baiev

Alexander Sosnovshchenko

View More author details

Table of Contents (14) Chapters

Preface

1. Getting Started with Machine Learning FREE CHAPTER

2. Classification – Decision Tree Learning

3. K-Nearest Neighbors Classifier

4. K-Means Clustering

5. Association Rule Learning

6. Linear Regression and Gradient Descent

7. Linear Classifier and Logistic Regression

8. Neural Networks

9. Convolutional Neural Networks

10. Natural Language Processing

11. Machine Learning Libraries

12. Optimizing Neural Networks for Mobile Devices

13. Best Practices

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

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Authors (3)

Alexander Sosnovshchenko

Alexander Sosnovshchenko

Alexander Sosnovshchenko has been working as an iOS software engineer since 2012. Later he made his foray into data science, from the first experiments with mobile machine learning in 2014, to complex deep learning solutions for detecting anomalies in video surveillance data. He lives in Lviv, Ukraine, and has a wife and a daughter.

See other products by Alexander Sosnovshchenko

Jojo Moolayil

Jojo Moolayil

Jojo Moolayil is an artiﬁcial intelligence, deep learning, machine learning, and decision science professional with over six years of industrial experience. He is the author of Learn Keras for Deep Neural Networks, published by Apress, and Smarter Decisions – The Intersection of IoT and Decision Science, published by Packt Publishing. He has worked with several industry leaders on high-impact, critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist in Canada. Apart from writing books on AI, decision science, and the internet of things, Jojo has been a technical reviewer for various books in the same ﬁelds published by Apress and Packt Publishing.

See other products by Jojo Moolayil

Oleksandr Baiev

Oleksandr Baiev

See other products by Oleksandr Baiev

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