Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, 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 (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

K-means clustering – problems


Refer to the following for more information about k-means and k-means++:

  • https://en.wikipedia.org/wiki/K-means_clustering
  • https://en.wikipedia.org/wiki/K-means%2B%2B

K-means algorithm suffers from at least two shortcomings:

  • The worst-case time complexity of the algorithm is super polynomial in the input size, meaning that it is not bounded above by any polynomial
  • Standard algorithm can perform arbitrarily poor in comparison to the optimal clustering because it finds only an approximation of the real optimum

Try it out yourself: put four pins on a map, as shown in the following image. After running clustering several times, you may notice that the algorithm often converges to the suboptimal solution:

Figure 4.4: Optimal and non-optimal clustering results on the same dataset