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)

Basic Facial Detection

The previous chapters can best be described as trying to read an image. This is a subfield in machine learning called computer vision (CV). With convolutional neural networks (Chapter 7, Convolutional Neural Networks – MNIST Handwriting Recognition), we found that the convolutional layers learned how to filter an image.

There is a common misconception that any machine learning (ML) worth doing has to come from neural networks and deep learning. This is decidedly not the case. Instead, one should view deep learning as a technique to get to one's goals; deep learning is not the end-all. The purpose of this chapter is to expose readers to some of the insights into making ML algorithms work better in production. The code for this chapter is exceedingly simple. The topic is trivial and widely considered by many to be solved. However, the insights...