🕸️ Neural Networks

A neural network is a layered set of connected numerical operations that turns input data into an output such as a label, score or prediction.

A small calculation step in a neural network that combines input numbers, adjusts them by learned settings, and passes forward a new number.

A comparison between neural networks and brains that can help with intuition, but breaks down because AI systems use simplified maths rather than living cells and biological processes.

These layers organise how a neural network turns input numbers into intermediate calculations and then into an output such as a score, label or predicted value.

A number attached to a model’s possible outputs that shows how much of its total estimated likelihood the model assigns to each option.

A weight is a learned number that scales how strongly one input signal affects the next calculation in a neural network.

A neural network usually uses an activation function to transform a unit’s weighted input into an output, often adding the non-linear behaviour needed to learn complex patterns.

A bias is an extra adjustable number added to a neuron's weighted input so the neuron's output can shift even when all inputs are zero.

A layered model that reuses small filters across a grid-like input, combines local patterns into larger features, and produces task outputs from them.

The calculation that carries input numbers through a neural network’s layers to produce an output such as a score, label or next-token distribution.

A recurrent neural network processes a sequence by carrying a changing hidden state from one step to the next, so earlier inputs can influence later outputs.

A numerical function that turns a list of model scores into positive values that sum to 1, highlighting which outputs are larger relative to the others.