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


  • Develop your own Kafka producer that will write JSON records with three fields to a Kafka topic.
  • A very interesting statistical property is covariance. Find its formula and implement it in Go.
  • Change the code of stats.go in order to work with integer values only.
  • Modify cluster.go in order to get the data from an external file that will be given as a command-line argument to the program.
  • Change the code of outlier.go in order to divide the input into two slices and work with each one of these slices.
  • Change the code of outlier.go in order to accept the upper and lower limits from the user without calculating them.
  • If you find TensorFlow difficult to use, you can try tfgo, which can be found at