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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

Deep learning frameworks

There are a plenty of deep learning toolkits and libraries for different kinds of platforms. For a long time, the three most popular of them were Theano (Python), Torch (Lua), and Caffe (C++). Somehow, Caffe became an industrial standard, while Theano and Torch were mostly used among researchers. I call these three libraries the first generation of deep learning frameworks. Most of the pre-trained neural networks that are available on the internet are still in Caffe format. They had their own problems, so the next generation of frameworks followed in several years. If the first generation was created mainly by efforts of individual researchers, the second generation was pushed by big IT companies. Today, apart from Apple, every internet giant has its own open source deep learning framework: Google has TensorFlow and Keras, Microsoft has CNTK, Facebook...

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