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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

Convolutional Neural Networks

 In this chapter, we are discussing the convolutional neural networks (CNNs). At first we are going to discuss all components with examples in Swift just to develop an intuition about the algorithm and what is going on under the hood. However, in the real life you most likely will not develop CNN from scratch, because you will use some ready available and battle-tested deep learning framework.

So, in the second part of the chapter we will show a full development cycle of deep learning mobile application. We are going to take the photos of people's faces labeled with their emotions, train a CNN on a GPU workstation, and then integrate it into an iOS application using Keras, Vision, and Core ML frameworks.

To the end of this chapter you will have learned about:

  • Affective computing
  • Computer vision, its tasks, and its methods
  • CNNs, their anatomy...
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