Building A Neural Network from Scratch in Go

Foreword

Neural networks have revolutionized many fields, from image recognition to natural language processing. But for beginners, the high-level libraries and frameworks can sometimes obscure the core mechanics of how these algorithms work. In this long and technical blog article, we will build a fully functioning neural network for the XOR problem from scratch — in the Go programming language.

We will explore the mathematics behind feedforward networks, walk through the code line by line, discuss use cases for such a network, outline the benefits of implementing it yourself, and finally examine the results. By the end, you will have a deep appreciation for both the conceptual underpinnings and practical implementation details of basic neural networks in Go.

1. Introduction

Developing a neural network from scratch can feel like climbing a steep mountain: you need to understand the mathematics, the data flow, the update mechanisms, and the code structure. However, once you reach the top of that mountain, you gain unparalleled insight into how these fascinating models truly function. In most tutorials, you might see high-level frameworks like TensorFlow, PyTorch, or Keras. While they are excellent for practical applications, they often abstract away essential details — such as…