A Neural Network is a computing system loosely inspired by the human brain, made up of interconnected “neurons” (simple processing units) organised in layers. It’s the fundamental building block of deep learning and modern AI.
What it means in plain English
A neural network takes some input (an image, a sentence, a set of numbers), passes it through layers of interconnected nodes, and produces an output (a prediction, a classification, a piece of text). Each connection between nodes has a “weight” — a number that controls how much influence one node has on the next. Learning, in a neural network, is the process of adjusting all these weights until the network produces good outputs.
The brain analogy is loose and worth not taking too literally. Artificial neurons are simple mathematical functions, not biological cells, and a neural network doesn’t “think.” But the idea of many simple units working together to produce complex behaviour is a genuinely useful way to picture what’s happening.
A simple example
Imagine a network that decides whether a photo contains a cat. Pixels go in one side; the network passes them through its layers, each detecting more abstract features; and a single value comes out the other side representing “cat” or “not cat.” During training, whenever it guesses wrong, the weights are nudged so it’s more likely to be right next time — repeated across millions of images until it’s reliably accurate.
Why it matters
Neural networks are the structure that nearly all modern AI is built on. When a network has many layers, we call the approach deep learning; when it’s arranged in the specific design behind chatbots, we call it a transformer. Every large language model and image generator is, at its core, an enormous neural network with billions of tuned weights.
Related terms
- Deep Learning — using neural networks with many layers.
- Parameter — another word for the weights a network learns.
- Backpropagation — the algorithm used to adjust the weights during training.
- Transformer — the neural-network design behind modern language models.
Frequently asked questions
What is a neural network?
A neural network is a model loosely inspired by the brain, made of layers of connected “neurons” that learn to transform inputs into outputs by adjusting connection weights.
What is a “deep” neural network?
A deep neural network has many layers between input and output, letting it learn increasingly complex features — the basis of deep learning.
