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