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Propositional truth tables:
There {X\t} is a substitution that replaces all occurances of variable X with a term representing an object t as:
By repeating this for all the quantifiers than we get a set of ground formulae in which we have to check that if the original sentence is true or false. But unfortunately, we don't have specified there that our domain Δ is finite - like in example we see that it may contain the natural numbers - but there may be a infinite number of sentences to check for a given model! So there may be also be an infinite number of models. And although we have a proper definition of model hence a proper semantics for first-order logic and we can't rely on having a finite number of models like we did where drawing propositional truth tables.
2) Consider the following neural network for two predictors Thickness and Alignment and two classes Print Quality High and Low. Some weights are shown in the table, including weigh
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what are the fundamental data structures
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