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Machine Learning with Swift

Machine Learning with Swift

By : Alexander Sosnovshchenko , Jojo Moolayil, Oleksandr Baiev
3 (1)
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Machine Learning with Swift

Machine Learning with Swift

3 (1)
By: Alexander Sosnovshchenko , Jojo Moolayil, Oleksandr Baiev

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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K-Means Clustering

In this chapter, we're going to switch our attention from supervised learning to unsupervised learning. The algorithms that we'll discuss and implement in this chapter are k-means and k-means++ clustering.

In this chapter, we will cover the following topics:

  • Instance-based algorithm of k-means clustering
  • The shortcomings of the k-means and how to fix them with the k-means++
  • Where you can use k-means and where you shouldn't use it
  • Application of clustering for signal quantization
  • How to choose the number of clusters
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