Binary cross-entropy is a loss function used to measure the difference between the predicted probabilities and the actual binary outcomes in classification tasks. This function is crucial for evaluating models in tasks where the output is a probability, as it penalizes incorrect predictions more heavily based on the confidence of the predictions. It plays a significant role in model training, particularly in neural networks designed for binary classification problems and also influences the architecture and effectiveness of autoencoders and variational autoencoders.
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