AI Vocabulary · 01 The Model
What is Neural Network?
A layered structure of simple calculations, loosely inspired by the brain, that learns patterns from data.
A neural network is a way of building a machine learning model out of many small, connected calculations arranged in layers. Each connection has a number attached, a weight, and those numbers are the model's parameters. During training, the weights are adjusted step by step until the network's outputs match the examples well.
The name comes from a loose analogy with neurons in the brain, but the similarity is mostly historical. In practice, a neural network is a very large amount of arithmetic. Deep learning simply means networks with many layers. Today's large language models are neural networks built on the transformer design.
Why it matters for you
You don't need to know the maths. The useful takeaway: a neural network finds patterns, it does not follow explicit rules, so it can be impressively flexible and hard to explain at the same time.
Own it
Because the reasoning is spread across billions of weights, you can't simply read why a decision was made. Build in checks and human review wherever an explanation matters.
Where it comes from
The idea goes back to 1943, when Warren McCulloch and Walter Pitts described a simple mathematical model of a nerve cell in "A Logical Calculus of the Ideas Immanent in Nervous Activity". In 1957 Frank Rosenblatt built on it with the perceptron. His Mark I Perceptron, a machine that "saw" through 400 light sensors, is now held by the Smithsonian's National Museum of American History. Artificial neuron · Perceptron
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