---
title: "What Is a Neural Network? A Plain-English, Sourced Explainer"
slug: what-is-a-neural-network
category: ai
category_label: "AI"
author: "BrainWavePost Staff"
date: 2026-06-06
tags: ["neural networks", "deep learning", "AI basics"]
read_time_minutes: 6
canonical_url: https://brainwavepost.com/article/what-is-a-neural-network
source: BrainWavePost
---

# What Is a Neural Network? A Plain-English, Sourced Explainer

*AI · 2026-06-06 · BrainWavePost Staff · 6 min read*

> A short, fully sourced introduction to neural networks using official definitions from IBM and educational material from Google.

> **How this article is sourced** _(info)_
>
> Definitions here come from IBM's official 'What is a neural network?' page and Google's Machine Learning Crash Course. [1][2]

IBM defines a neural network as 'a machine learning model that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.' [1]

## How it is structured

IBM explains that a neural network is organised into layers of connected nodes ('neurons'): an input layer that receives the data, one or more hidden layers that transform it, and an output layer that produces the prediction. Each connection has a numerical weight that the network adjusts as it learns. [1]

## What 'deep learning' means

When a neural network has many hidden layers, it is called a deep neural network — the technique known as deep learning. IBM describes deep learning as a subset of machine learning that uses these multi-layered networks. [1]

## How a network learns

Google's Machine Learning Crash Course explains the basic loop: the network makes a prediction, compares it to the correct answer using a loss function, and uses an algorithm called backpropagation with gradient descent to adjust its weights so that future predictions are a little more accurate. [2]

## What neural networks are commonly used for

- Image recognition and computer vision. [1]
- Speech recognition and natural language processing. [1]
- Recommendation systems. [1]

> **Mental model** _(tip)_
>
> Think of a neural network as a stack of adjustable filters: each layer transforms the input a little, and training is the process of nudging those filters until the final output is usually right. [1][2]

## References (clickable)

- [1] IBM — What is a neural network?: https://www.ibm.com/topics/neural-networks
- [2] Google — Machine Learning Crash Course, Neural Networks: https://developers.google.com/machine-learning/crash-course/neural-networks/anatomy

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