Neural Network

A neural network is a type of computing system designed to recognise patterns. It is loosely inspired by how neurons in the human brain connect to each other — but the analogy stops there. A neural network is software, not biology.

The basic structure

A neural network is organised in layers:

• Input layer: Where data enters the system. This could be pixels from an image, words from a sentence, or numbers from a spreadsheet.
• Hidden layers: Where the actual pattern recognition happens. Each layer transforms the data, extracting increasingly abstract features. A single hidden layer might detect edges in an image; deeper layers might detect faces.
• Output layer: Where the result comes out — a classification ("this is a cat"), a prediction ("tomorrow's sales will be £12,000"), or generated content ("here is your email draft").

Each connection between nodes has a weight — a number that determines how much influence one node has on the next. During training, these weights are adjusted millions or billions of times until the network produces accurate results.

How neural networks learn

Neural networks learn through a process called training. You feed the network thousands or millions of examples along with the correct answers. The network makes predictions, compares them to the correct answers, measures the error, and adjusts its weights to reduce that error. This cycle repeats until the network's predictions are consistently accurate.

This is fundamentally different from traditional programming, where a developer writes explicit rules. With neural networks, the system discovers its own rules from the data. This is why neural networks can tackle problems that are too complex to program manually — like recognising speech in a noisy room or understanding sarcasm in text.

Types of neural networks

Different architectures suit different problems:

• Feedforward networks: The simplest type. Data flows in one direction, input to output. Good for basic classification.
• Convolutional neural networks (CNNs): Designed for image and video processing. They scan data in small patches, making them excellent at visual pattern recognition.
• Recurrent neural networks (RNNs): Designed for sequential data like text or time series. They have a form of memory that lets them consider previous inputs when processing current ones.
• Transformers: The architecture behind modern LLMs. They process all parts of the input simultaneously (not sequentially) and use an "attention" mechanism to focus on the most relevant parts. This is the breakthrough that made ChatGPT and Claude possible.

Scale matters

The power of neural networks scales with size — more layers, more nodes, more training data, and more computing power generally produce better results. This is why modern AI requires enormous computing resources and why AI companies invest billions in GPU clusters.