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You already met a single weight, back when we watched a dog decide whether to bark. Smell and sound each fed into the decision, and each one had a weight: how much that sense counts toward barking. A strong smell should push hard, a faint background noise barely at all.
That is all a weight is: a dial controlling how much one input counts. Turn it up and the input matters. Leave it near zero and it barely registers. Turn it below zero and it argues against. Drag the dial and watch. Nobody sets these numbers by hand. Training finds them.
So that is one weight. Now the part worth naming clearly.
You will often hear models measured in parameters rather than weights. They overlap, but they are not quite the same word. A weight is the dial on a connection, how much one input counts. Parameters is the umbrella term for every number training adjusts, and that includes one more kind: the bias.
A bias is a neuron's baseline eagerness to fire, before any input arrives. Remember the dog's threshold, the bar a signal had to clear to set off a bark. A bias raises or lowers that bar. A high bias means the neuron fires easily, a low one means it stays quiet unless the inputs really pile up. Like weights, biases are just numbers, and training tunes them too.
So when you hear that a model has 70 billion parameters, that means 70 billion learned numbers, mostly weights, plus a bias for each neuron. Each one a small decimal. Each one found by training.
And here is where the simple picture breaks. With the dog, you could read each dial: this one watches smell, that one watches sound. In a real model, with billions of them, that tidy story falls apart. No single weight watches any one thing.