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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

Convolution operation


Convolution is one of the most important operations in the image processing. Blurring, sharpening, edge detection, denoising, embossing and many other familiar operations in image editors are actually convolutions. It is similar to the pooling operation in some way, because it is also a sliding window operation, but instead of taking the average over the window, it performs element-wise multiplication by the kernel – matrix of size n × n and sums the result. The result of the operation depends on the kernel (also known as convolution filter) – a matrix, which is usually square, but not necessarily, see Figure 9.3. The notions of the stride and padding are the same as in the pooling case:

Figure 9.3: Different convolution filters have different effects on the picture

Convolution operation works in the following way (see the following diagram):

  • The convolution kernel (filter) slides over the image from left to right, and from top to bottom
  • At each position, we calculate an...
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