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)

MachineBox

As mentioned, we will not focus on the math going on behind the scenes of face detection. Instead, we will use an external service to perform the recognition for us. The external service is MachineBox. What it does is quite clever. Instead of having to write your own deep learning algorithms, MachineBox packages up the commonly-used deep learning functionalities into containers, and you simply just use them straight out of the box. What do I mean by commonly-used deep learning functionalities? Nowadays people are relying more and more on deep learning for tasks such as facial recognition.

Just like Viola-Jones in the early 2000s, there are only a few commonly used models—we used the Haar-like cascades generated by Rainer Lienhart in 2002. The same is becoming true of deep learning models, and I shall talk more about the implications of that in the next chapter...