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What is Cross Entropy Loss Explained about?

This document introduces cross entropy loss, which is used to calculate the loss between predicted probabilities and true labels in neural networks. It explains how to calculate cross entropy loss step-by-step using examples, including computing softmax probabilities from logits, calculating loss for individual data points, and calculating total loss over all data. Cross entropy loss aims to minimize the distance between predicted probabilities and true labels.

Author

Chandra kusuma dewa

Language

EN