Neural Network

Simple Definition

A neural network is a type of AI model made up of layers of connected mathematical units called nodes (or neurons). It’s loosely inspired by how the brain works, though in practice it’s more like a very complex math function than a biological brain.

Neural networks are the foundation of most modern AI — including language models, image generators, and voice assistants.

How a Neural Network Works

A neural network has three types of layers:

1. Input layer — receives the raw data (text, image pixels, audio)
2. Hidden layers — process and transform the data through multiple stages
3. Output layer — produces the final result (a word, a category, a prediction)

Each connection between nodes has a weight — a number that says how much influence that connection has. Training adjusts all these weights until the network produces correct outputs.

A Simple Analogy

Think of a neural network like a series of filters. Each layer filters the input into something more refined. By the time the data reaches the output, the network has distilled raw input into a meaningful result.

Why They’re Powerful

Neural networks can learn patterns that would be impossible to program manually. A network trained on millions of images can learn to recognize faces — something that would take years to code with rules.

Common Types

• Feedforward networks — the basic type, data flows one direction
• Convolutional networks (CNNs) — specialized for images
• Recurrent networks (RNNs) — designed for sequences, older language models
• Transformers — the architecture behind modern LLMs

Related Terms

• Deep Learning — neural networks with many layers
• Transformer — the dominant neural network architecture today
• Machine Learning — the broader field
• LLM — large language models built on neural networks