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 is a face?

In order to detect faces, we need to understand what a face is, specifically what a human face is. Think about a typical human face. A typical human face has two eyes, a nose, and a mouth. But having these features isn't enough to define a human face. Dogs also have two eyes, a nose, and a mouth. We are, after all, products of mammalian evolution.

I encourage the reader to think more carefully about what makes a human face. We instinctively know what a face is, but to really quantify exactly what constitutes a face takes work. Often, it may lead to philosophical ruminations about essentialism.

If you watch terrible procedural TV shows, you might see faces being drawn with dots and lines when the detectives on TV are doing facial recognition across a database. These dots and lines are primarily due to the work of Woodrow Bledsoe, Helen Chan, and Charles Bisson...