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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 learned about iOS compatible machine learning libraries and their features. We discussed five major deep learning frameworks: Caffe, TensorFlow, MXNet, and Torch, and so on. We also mentioned several smaller deep learning libraries and tools to convert deep learning models from one format to another. Among general purpose machine learning libraries, the most feature-rich and mature are AIToolbox, dlib, Shark, and YCML. NLP libraries for iOS are rare and restricted in their capabilities.

In addition to native iOS speech recognition and text-to-speech, there are several free and commercial libraries that provide the same functionality.

If you have some common computer vision tasks, you can find the appropriate algorithms in OpenCV or ccv libraries. OCR and all kinds of face-related tasks can also be performed using the open source toolchain. There are...

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