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Machine Learning with Swift

Machine Learning with Swift

By : Alexander Sosnovshchenko
3 (1)
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Machine Learning with Swift

Machine Learning with Swift

3 (1)
By: Alexander Sosnovshchenko

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (14 chapters)
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Computer vision

Under this section let us look at few computer vision libraries in detail:

OpenCV

OpenCV is a library of computer vision algorithms, image processing, and general-purpose numerical algorithms. It is implemented in C/C++ but has interfaces for Python, Java, Ruby, Matlab, Lua, and other languages. It can be freely used for academic and commercial purposes because it is distributed under the BSD license.

Since OpenCV 3.1, there is a DNN module and in OpenCV 3.3, the module has been promoted to opencv_contrib.

Official site: http://opencv.org
Additional OpenCV modules: https://github.com/opencv/opencv_contrib
Demo Swift app using OpenCV: https://github.com/foundry/OpenCVSwiftStitch
...
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Machine Learning with Swift
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