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Loss Functions

A scalar measure of how wrong a prediction is, chosen to be differentiable so gradients exist everywhere. The loss defines the objective; the optimizer merely descends it.

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Loss Functions - EXPLAINED!

Loss functions serve as differentiable surrogate objectives in optimization theory that map prediction errors to scalar values for minimization via gradient descent or sub-gradients. The core mechani…