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

Neural networks

Neural networks, which try to work like the human brain, learn to perform tasks based on given examples. Neural network have layers, and the smallest neural network must have at least two layers: input and output. During the training phase, data flows through the layers of the neural network. The actual output values of the training data are used to correct the calculated output values of the training data so that the next iteration will be more precise.

The utility that will be developed in this section is named neural.go, and it will implement a really simple neural network. This is going to be presented in four parts.

The first part of neural.go is as follows:

package main 
 
import ( 
    "fmt" 
    "math/rand" 
    "time" 
 
    "github.com/goml/gobrain" 
) 

The new line in the import list tells the gofmt tool to sort...