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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

Using neurons to build logical functions

Among other obscured parts of iOS and macOS SDK, there is one interesting library called SIMD. It is an interface for direct access to vector instructions and vector types, which are mapped directly to the vector unit in the CPU, without the need to write an assembly code. You can reference vector and matrix types as well as linear algebra operators defined in this header right from your Swift code, starting from 2.0 version.

Note

The universal approximation theorem states that a simple NN with one hidden layer can approximate a wide variety of continuous functions if proper weights are found. This is also commonly rephrased as NNs as universal function approximators. However, the theorem doesn't tell if it's possible to find such proper weights.

To get access to those goodies, you need to import simd in Swift files, or #include <simd/simd.h> in C/C++/Objective-C files. GPU also has SIMD units in it, so you can import SIMD into your metal shader...

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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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