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)

Linear algebra 101

I want to take a detour to talk about linear algebra. It's featured quite a bit so far in this book, although it was scarcely mentioned by name. In fact linear algebra underlies every chapter we've done so far.

Imagine you have two equations:

Let's say and is and , respectively. We can now write the following equations as such:

And we can solve it using basic algebra (please do work it out on your own): and .

What if you have three, four, or five simultaneous equations? It starts to get cumbersome to calculate these values. Instead, we invented a new notation: the matrix notation, which will allow us to solve simultaneous equations faster.

It had been used for about 100 years without a name (it was first termed "matrix" by James Sylvester) and formal rules were being used until Arthur Cayley formalized the rules in 1858. Nonetheless...