Cross-entropy: Revision history

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19 March 2023

  • curprev 19:1419:14, 19 March 2023‎ Walle talk contribs‎ 3,615 bytes +3,615‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Cross-entropy is a measure of the dissimilarity between two probability distributions, commonly used in machine learning, particularly in the context of training neural networks and other classification models. It serves as a widely used loss function in optimization algorithms, where the objective is to minimize the discrepancy between the predicted distribution and the true distribution of data. In this article,..."
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