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)

The researcher, the practitioner, and their stakeholder

A word on scale—there is a tendency to reach out to packages or external programs, such as Spark, to solve the problem. Often they do solve the problem. But it's been my experience that ultimately, when doing things at scale, there is no one-size-fits-all solution. Therefore, it's good to learn the basics, so that when necessary, you may refer to the basics and extrapolate them to your situation.

Again on the topic of scale—both researchers and practitioners would do well to learn to plan projects. This is one thing that I am exceedingly bad at. Even with the help of multiple project managers, machine learning projects have a tendency to spiral out of control. It does take quite a bit of discipline to manage these. This is both on the implementor's part and on the stakeholder's part.

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