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

Calculating simple statistical properties

Statistics is an area of mathematics that deals with the collection, analysis, interpretation, organization, and presentation of data. The field of statistics is divided into two main areas: the area of descriptive statistics, which tries to describe an already existing group of values, and the area of inferential statistics, which tries to predict upcoming values based on the information found in the current set of values.

Statistical learning is a branch of applied statistics that is related to machine learning. Machine learning, which is closely related to computational statistics, is an area of computer science that tries to learn from data and make predictions about it without being specifically programmed to do so.

Statistical models try to interpret data as accurately as possible. However, the accuracy of a model might depend on...