Neural Networks from Scratch in Go – Tutorial

Welcome to this tutorial on building neural networks from scratch in Go. In this tutorial, we will learn how to implement a basic neural network using the Go programming language. This tutorial assumes that you have some basic knowledge of Go and machine learning concepts.

Introduction

Neural networks are a fundamental concept in machine learning and artificial intelligence. They are made up of neurons that are connected in layers, and each neuron performs some computation to produce an output. By training these networks on labeled data, we can make them learn patterns and make predictions on new data.

Getting Started

Before we can start building our neural network, we need to set up our environment. Make sure you have Go installed on your computer. You can download it from the official website if you haven’t already.

Step 1: Installing Dependencies

First, we need to install the Go “gonum” library, which provides support for numeric calculations and linear algebra operations. You can install it using the following command:

go get -u gonum.org/v1/gonum/mat

Step 2: Creating Our Neural Network

Now, let’s start by creating our neural network struct in Go. We will define the structure of our network, including the number of input and output neurons, the number of hidden layers, and the activation function to be used.


type NeuralNetwork struct {
// Define the structure of our neural network here
}


Step 3: Implementing Forward and Backward Propagation

Next, we need to implement the forward and backward propagation methods for our neural network. Forward propagation is the process of passing input data through the network to compute the output, while backward propagation is used to update the weights of the network based on the error calculated during training.


func (nn *NeuralNetwork) ForwardPropagation(inputs []float64) []float64 {
// Implement forward propagation here
}

func (nn *NeuralNetwork) BackwardPropagation(inputs []float64, targets []float64) {
// Implement backward propagation here
}


Conclusion

Congratulations! You have successfully learned how to build a basic neural network from scratch in Go. We have covered the fundamentals of neural networks, including forward and backward propagation. You can now further explore different architectures and optimization techniques to improve the performance of your neural network.

Thank you for following along with this tutorial. Happy coding!