Clean cross in binary smoothly

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Aug 6th, 2022
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How to Clean cross in binary

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hey guys welcome to digital screening channel on youtube and in this short video im going to talk about binary cross entropy using plain english mostly and a very little bit of math and the reason im talking about this is binary cross entropy of course we use this as a loss function for classification purposes and even in semantic segmentation and of course even in generative adversarial networks if you actually look at your discriminators or generators this is a quite common loss function that we use so it only makes sense for us to understand this a little bit better so lets jump in and before doing that a quick request whenever you watch any of my videos for example in this case this is one of the recent videos go ahead and hit the like button if you really like it and that tells me what type of content youre actually into so ill put more effort into liking those and also the style or the way that i cover these topics so feedback is very important also do subscribe to my chann

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How to normalise a floating point number Moving the binary point one place to the right is equal to a left shift that multiplies the number by 2. Moving the binary point one place to the left is equal to a right shift that divides the number by 2.
Some researchers are in favor of standardizing binary variables as it would make all predictors on same scale. It is a standard practice in penalized regression (lasso).
binarycrossentropy: Used as a loss function for binary classification model. The binarycrossentropy function computes the cross-entropy loss between true labels and predicted labels. categoricalcrossentropy: Used as a loss function for multi-class classification model where there are two or more output labels.
There is no need to normalize categorical variables. You are not very explicit about the type of analysis you are doing, but typically you are dealing with the categorical variables as dummy variables in the statistical analysis.
Binary cross entropy compares each of the predicted probabilities to actual class output which can be either 0 or 1. It then calculates the score that penalizes the probabilities based on the distance from the expected value. That means how close or far from the actual value.
Cross entropy can be applied in both binary and multi-class classification problems. Well discuss the differences when using cross-entropy in each case scenario.
Normalizing dummy variables makes no sense. Usually, normalization is used when the variables are measured on different scales such that a proper comparison is not possible.
It is common practice to standardize or center variables to make the data more interpretable in simple slopes analysis; however, categorical variables should never be standardized or centered.

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