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

KNN cons

  • The algorithm is fast for training but slow for inference.
  • You need to choose the best k somehow (see Choosing a good k section).
  • With the small values of k, the model can be badly affected by outliers; in other words, it's prone to overfitting.
  • You need to choose a distance metric. For usual real value features, one can choose among many available options (see Calculating the distance section) resulting in different closest neighbors. The metric used by default in many machine learning packages is the Euclidean distance; however, this choice is nothing more than a tradition and for many applications is not the optimal.
  • Model size grows with the new data incorporated.
  • What should we do if there are several identical samples with different labels? In this case, the result can be different depending on the order in which samples are stored.
  • The model suffers from the curse of dimensionality.