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)

Why MachineBox?

I personally prefer to develop my own machine learning solutions. One may, of course, chalk this up to ego. However, in the first chapter, I introduced the notion that there are different types of problems. Some of these problems may be solved by machine learning algorithms. Some problems may only require general machine learning algorithms, while some require specialized algorithms derived from the general algorithms. In the majority of this book, I've shown the general algorithms, and readers are free to adapt these to their own specific problems.

I, too, recognize the value of having general machine learning algorithms as being part of the solution. Imagine that you are developing a program to reorganize your personal photos on your computer. There is no need to spend a protracted amount of time getting a convolutional neural network trained upon a corpus...