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Machine Learning with Swift

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

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787121515
Length 378 pages
Edition 1st Edition
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Authors (3):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Oleksandr Baiev Oleksandr Baiev
Author Profile Icon Oleksandr Baiev
Oleksandr Baiev
Alexander Sosnovshchenko Alexander Sosnovshchenko
Author Profile Icon Alexander Sosnovshchenko
Alexander Sosnovshchenko
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Table of Contents (14) Chapters Close

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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