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Take a breath. A lot has happened in this chapter. Let's make sure the foundations are solid before we build on them.
What a neuron does. It takes in several signals, multiplies each one by a weight, adds them all up, and fires if the total is high enough. That's the whole operation.
What a weight is. A number that controls how much a signal counts. High weight means "I take this seriously." Low weight means "I barely listen to this." The weights are where the neuron keeps its memory of what matters.
What training is. Making a guess, measuring how wrong it was, and nudging every weight slightly in the direction of a better answer. Do that thousands of times, and the weights slowly settle into something that works. Nobody sets them there. The machine corrects its way there, the same way you corrected your way to balance on a bike.
What the Perceptron was. One neuron with adjustable weights, learning from examples. A real advance, but with a hard ceiling built into the design.
What that ceiling was. One neuron can only score inputs independently. It has no way to ask what two inputs mean together. That isn't fixable with more training. It's a limit of the design itself.
Okay. Now we can go further.