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What does entropic loss mean?
In machine learning, entropic loss is a specific kind of loss function. A loss function measures the error between the predicted results and the desired results. Entropic loss uses the logistic function to show the error (or "loss") between the true answer and the predicted answer. A general loss function is defined here: Lφ= ∫ (φ(z) - y)dz, where the bounds of the integral are φ-1(y) (on the bottom) and φ-1(yhat) (on the top). yhat is the estimated (predicted) result, and y is the desired result. The entropic loss function is defined here: Lφ= ∫ (1/(1 + exp(-z)) - y)dz As you can see, the entropic loss is a specific instance of a loss function. All I've done is I've chosen my loss function φ(z) to be the logistic function. The logistic function is a common choice because it is a convex function, and therefore has only one local minimum (therefore it is also the global minimum). When we try to minimize loss, we want only one minimum so that when we set the derivative to zero, we know we've found what we want.
What is common-mode failure?
Loss of function in multiple structures, systems, and components in the same manner.
What does RAS mean and what is its function?
seduction
What is the functions f mushrooms?
if you mean f(mushrooms) then use whatever function on the variable or variable mushrooms. if you mean the function mushrooms, then i have no idea as i would assume there is no standard function mushrooms.
The purpose and function of standard error of measurement?
the purpose and function of standard error of mean
