The basic unit of an artificial neural network, loosely modelled on a brain cell. It takes in numbers, combines them using weights, and passes on a signal if the combined value is strong enough.

A system whose internal workings cannot be easily inspected or explained. Many neural networks are black boxes: they can be accurate while no one can fully explain how they reached a particular output.

The field of AI that enables machines to interpret images and video, such as recognising objects, faces, or medical features, often using deep neural networks.

A form of machine learning that uses artificial neural networks with many layers to learn complex patterns directly from large amounts of raw data, powering tasks such as image and speech recognition.

Computational models inspired by the human brain that use neurons to learn from data to perform tasks like classification, prediction, and pattern recognition.

The set of examples used to teach a machine learning model. The quality, quantity, and representativeness of the training data shape what the model learns and any bias it inherits.

A number that sets how strongly one connection in a neural network influences the next neuron. Learning means gradually adjusting these weights so the network produces better outputs.