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We've spent this whole module building a network: neurons in layers, connected by weights, slowly trained until the weights settle into useful values. So what do you actually have once the training stops?
You have a model.
The network is the machine: the layout of neurons and the rules for passing signals through it. The model is what that machine becomes once it's trained, the exact weights it landed on. Same structure, but now filled in with everything it learned. When people release "a model," this is what they mean: a network frozen at the moment it got good.
And when people talk about it, the word can sound like something grand. A system. A mind. Something that lives in the cloud.
But a model is a file. A plain file, sitting on a hard drive, the same way report.pdf or vacation-photos.zip does. When researchers share their work publicly, they publish that file (a huge one, often tens or hundreds of gigabytes). And if you opened it, here is what you would find inside:
Numbers. Billions of them. They are organized into groups and layers, but none of it looks like knowledge. No words. No rules. No instructions written by hand. Just numbers, each one a weight.
Those numbers are the model. Everything it knows, every pattern it learned, every capability it has, lives in that list. Nothing else.
But a list of numbers on its own doesn't do anything. There's a second piece, and without it, the numbers are meaningless.