Book Image

Go Machine Learning Projects

By : Xuanyi Chew
Book Image

Go Machine Learning Projects

By: Xuanyi Chew

Overview of this book

Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This book will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The book begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you'll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this book, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.
Table of Contents (12 chapters)

PICO

Another technique we'll be using is Pixel Intensity Comparison-based Object detection (PICO), originally developed by Markus, Frljak, et al. in 2014. It uses the same broad principles as the Viola-Jones method, in that there is a cascade classifier. It differs in two ways. First, a sliding window is not used. This is due to the latter differences. Second, the classifiers of the cascade classifier are different from that of Viola-Jones. In Viola-Jones, a method of applying filters repeatedly and then summing the result is used as a classifier. By contrast, in PICO, decision trees are used.

A decision tree is a tree where each node is a feature, and the branching of the feature is defined by a threshold. In the case of PICO, the decision tree applies for each pixel in the photo. For each pixel considered, the intensity is compared against the intensity of another pixel...