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Categorical Cross-Entropy Loss

The categorical cross-entropy loss is also known as the negative log likelihood. It is a popular loss function for categorization problems and measures the similarity between two probability distributions, typically the true labels and the predicted labels. It is given by L = -sum(y * log(y_prediction)) where y is the probability distribution of true labels (typically a one-hot vector) and y_prediction is the probability distribution of the predicted labels, often coming from a softmax.