Layers of simple multiply-and-add units wired together: the backbone of modern AI.
A neural network is a way of computing loosely inspired by nerve cells in the brain. Each unit, or neuron, does something simple: it multiplies the numbers coming in by weights, adds them up, and passes a signal on if the total is high enough.
Thousands or millions of these units are wired together in layers. Training a network just means adjusting the weight on each connection. It isn't a copy of a real brain; it borrowed the name and the basic idea. Describing neurons as math goes back to a 1943 paper.
Think of it like this
Imagine your class passing a note down a line. Each person adds a score using their own rule and hands it on. Nobody does anything clever alone, but what comes out at the end of the line can be a pretty smart decision.
For example
A classic beginner exercise is a network that looks at a handwritten digit and says which number, 0 to 9, it is.
Records about this
Source-checked moments from AI history.
Showed that hidden layers learn useful internal representations for a task on their own, making the training of multilayer neural networks practical
The first attempt to formalize the activity of nerve cells as logical operations, and the mathematical starting point of artificial neural networks