Book Image

Mastering Go - Second Edition

By : Mihalis Tsoukalos
Book Image

Mastering Go - Second Edition

By: Mihalis Tsoukalos

Overview of this book

Often referred to (incorrectly) as Golang, Go is the high-performance systems language of the future. Mastering Go, Second Edition helps you become a productive expert Go programmer, building and improving on the groundbreaking first edition. Mastering Go, Second Edition shows how to put Go to work on real production systems. For programmers who already know the Go language basics, this book provides examples, patterns, and clear explanations to help you deeply understand Go’s capabilities and apply them in your programming work. The book covers the nuances of Go, with in-depth guides on types and structures, packages, concurrency, network programming, compiler design, optimization, and more. Each chapter ends with exercises and resources to fully embed your new knowledge. This second edition includes a completely new chapter on machine learning in Go, guiding you from the foundation statistics techniques through simple regression and clustering to classification, neural networks, and anomaly detection. Other chapters are expanded to cover using Go with Docker and Kubernetes, Git, WebAssembly, JSON, and more. If you take the Go programming language seriously, the second edition of this book is an essential guide on expert techniques.
Table of Contents (20 chapters)
Title Page

Machine Learning in Go

The previous two chapters discussed topics related to network programming, TCP/IP, HTTPS, RPC, and the net package. This chapter will talk about machine learning in Go, including many interesting topics such as calculating statistical properties, classification, regression, clustering, anomaly detection, neural networks, outlier analysis, and working with Apache Kafka. However, as all these are huge topics that deserve a book on their own, this chapter will only scratch the surface and give you a quick introduction to them, as well as introduce you to some handy Go packages that can help you to do the job.

Notice that each machine learning technique has some theory behind it – knowing the theory, the parameters, and the limitations of the techniques you are trying to use is essential for the success of your work. Additionally, visualizing your data...