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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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Toc

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

Summary

In this chapter, we built a deep learning CNN, and trained it using Keras to recognize facial expressions on photos. Then we ported it for the mobile application using Core ML. The model can work in real time. We've also become acquainted with the Apple Vision framework.

CNNs are powerful tools that can be applied for many computer vision tasks, as well as for time-series prediction, natural language processing, and others. They are built around the concept of convolution—a mathematical operation that can be used for defining many types of image transformations. CNNs learn convolution filters in the similar manner as usual neural networks learn weights using the same stochastic gradient descent. Convolution requires less computations than usual matrix multiplications, which is why they can be effectively used on mobile devices. Apart from convolutional layers...

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