Machine Learning
Cross-Entropy Loss
A loss function commonly used in classification tasks to measure the difference between predicted and actual distributions.
Expanded definition
Cross-Entropy Loss quantifies the dissimilarity between the actual distribution of class labels and the predicted probability distribution produced by a model. It is particularly effective in tasks involving multiple classes and is widely used in training neural networks for classification problems. Minimizing cross-entropy loss during training helps improve the model's accuracy and performance.
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