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The short answer is that it makes the math easier in the long run. Here's the longer answer: Often in machine learning and in statistics we want to minimize the distance from the a predicted value and the actual/observed value. Like at 3:10 - we have measured the distance between the line and the data. If we want to find the straight line that minimizes those distances (not a squiggly line that fits them perfectly, but a straight line that's not perfect) then the easiest way to do that is to take the derivatives of the differences between the observed values and the values predicted by the line. Anyway, taking the derivatives of squared values is way easier than taking the derivatives absolute values, because absolute values are not continuous at 0, and thus, the derivative of an absolute value does not exit at 0.
youtube AI Bias 2019-10-13T00:4…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-27T06:26:44.938723
Raw LLM Response
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