Binary Cross-Entropy is a loss function commonly used in binary classification problems. It measures the dissimilarity between the true binary distribution and the predicted binary distribution.

Cross entropy tends to have a faster learning rate and convergence than mean squared error, because it has a steeper gradient when the predicted output is far from the true output.

Binary cross entropy is a loss function used for binary classification tasks (tasks with only two outcomes/classes). It works by calculating the following average: The above equation can be split into ...

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