Many interesting and useful phenomena are nonlinear in nature, meaning their inputs are not directly proportional to their outputs. When we wish to model these systems, our task becomes much easier when we can linearize them, which is the task of making an approximation of the overall function in a small region where a simpler model is close enough to be accurate.
The teaching of this card is to shift something which might be complex and intractable on a macro scale to something which is solvable and still valid in your local, immediate circumstances. Assuming we are navigating something which is nonlinear, the solution we find at this point will almost certainly not apply elsewhere. Not to worry, though, because we will just apply the same procedure again based on where we end up!
Watch an example of where linearization can be useful.
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