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Mastering Go

Mastering Go - Second Edition

By : Mihalis Tsoukalos
4.1 (33)
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Mastering Go

Mastering Go

4.1 (33)
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 (15 chapters)
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Updating the statistics application

In this section, we are going to improve the functionality and the operation of the statistics application. When there is no valid input, we are going to populate the statistics application with ten random values, which is pretty handy when you want to put lots of data in an application for testing purposes—you can change the number of random values to fit your needs. However, keep in mind that this takes place when all user input is invalid.

I have randomly generated data in the past in order to put sample data into Kafka topics, RabbitMQ queues and MySQL tables.

Additionally, we are going to normalize the data. Officially, this is called z-normalization and is helpful for allowing sequences of values to be compared more accurately. We are going to use normalization in forthcoming chapters.

The function for the normalization of the data is implemented as follows:

func normalize(data []float64, mean float64, stdDev...
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Mastering Go
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