Cross-Entropy

Browse all content tagged with Cross-Entropy

Glossary

Cross-Entropy

Cross-entropy is a pivotal concept in both information theory and machine learning, serving as a metric to measure the divergence between two probability distributions. In machine learning, it is used as a loss function to quantify discrepancies between predicted outputs and true labels, optimizing model performance, especially in classification tasks.

Cross-Entropy Machine Learning Loss Function +2
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