Abstract Cross-entropy loss (CEL) is a cornerstone in modern machine learning, particularly for classification problems. It is a critical component in training deep learning models, optimizing predictions by quantifying the dissimilarity between predicted probabilities and actual labels. This dissertation explores CrossEntropyLoss in depth, beginning with its mathematical foundations and progressing to its implementation in advanced … Continue reading CrossEntropyLoss: loss function design