#### 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.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Getting Started with Machine Learning
Classification – Decision Tree Learning
K-Nearest Neighbors Classifier
K-Means Clustering
Association Rule Learning
Linear Classifier and Logistic Regression
Neural Networks
Convolutional Neural Networks
Natural Language Processing
Machine Learning Libraries
Optimizing Neural Networks for Mobile Devices
Best Practices
Index

## Fixing linear regression problems with regularization

As we've seen, one outlier is enough to break the least-squares regression. Such instability is a manifestation of overfitting problems. Methods that help prevent models from overfitting are generally referred to as regularization techniques. Usually, regularization is achieved by imposing additional constraints on the model. This can be an additional term in a loss function, noise injection, or something else. We've already implemented one such technique previously, in Chapter 3, K-Nearest Neighbors Classifier. Locality constraint w in the DTW algorithm is essentially a way to regularize the result. In the case of linear regression, regularization imposes constraints on the weights vector values.

### Ridge regression and Tikhonov regularization

Under the standard least squares method, the obtained regression coefficients can vary wildly. We can formulate the least squares regression as an optimization problem:

What we have on the right here...