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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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Revisiting the classification task


We already used and implemented some classification algorithms in the previous chapters: decision tree learning, random forest, and KNN are all well suited for solving this task. However, as Boromir used to say,

"One cannot simply walk into neural networks without knowing about logistic regression"

. So, to remind you, classification is almost the same as regression, except that response variable y is not a continuous (float) but takes values from some set of discrete values (enum). In this chapter, we're primarily concerned with the binary classification, where y can be either true or false, one or zero, and belong to a positive or negative class.

Although, if you think about this for a moment, it's not too hard to build a multiclass classifier from several binary classifiers by chaining them one after the other. In the classification domain, response variable y is usually calledlabel.

Linear classifier

Linear regression can be trivially adapted for binary...

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