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)

What does this all mean?

Does this mean that MachineBox's algorithm is not good? The short answer is no: we cannot say that the MachineBox algorithm is not good. The longer answer requires a more nuanced understanding that combines engineering understanding and an understanding of machine learning. As far as the algorithm of facebox goes, there are no exact details about what facebox is composed of. But we can deduce what goes on.

First, note that the images with matches are all over 50% in their confidence. We can then assume that facebox considers a match being found only if the confidence level greater than 50%. I verified this by running the recognizer on a directory of over 1,000 images of faces. Only those that are matched have a greater-than 50% confidence. The program is as follows:

 func testFacebox() error {
files, err := filepath.Glob("OtherFaces/*&quot...