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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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Feature scaling


If you have several features and their ranges differ significantly, many machine learning algorithms may have taught times with your data: the large feature may overwhelm the features with small absolute values. A standard way to deal with this obstacle is feature scaling (also known as feature/data normalization). There are several methods to perform it, but the two most common are rescaling and standardization. This is something you want to do as a preprocessing step before feeding your data into the learner.

The least squares method is almost the same as the Euclidean distance between two points. If we want to calculate how close two points are, we want each dimension to make an equal contribution to the result. In the case of the linear regression features, contributions depend on absolute values of each feature. That's why feature scaling is a must before linear regression. Later, we will meet similar technique batch normalization when we talk about deep learning neural...

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