SOLVED: Show that for an example (€,y), the Softmax cross-entropy loss is: LscE(y,k) = - Yk log(yk) ≈ -yt log yk where log represents element-wise log operation. Show that the gradient of
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![DL] Categorial cross-entropy loss (softmax loss) for multi-class classification - YouTube](https://i.ytimg.com/vi/ILmANxT-12I/hq720.jpg?sqp=-oaymwEhCK4FEIIDSFryq4qpAxMIARUAAAAAGAElAADIQj0AgKJD&rs=AOn4CLDP2Mcvs9IEnETkFGUgaLNZ2t-iGg "DL] Categorial cross-entropy loss (softmax loss) for multi-class classification - YouTube")
DL] Categorial cross-entropy loss (softmax loss) for multi-class classification - YouTube
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Cross-Entropy Loss Function. A loss function used in most… | by Kiprono Elijah Koech | Towards Data Science
Cross-Entropy Loss Function. A loss function used in most… | by Kiprono Elijah Koech | Towards Data Science
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Why Softmax not used when Cross-entropy-loss is used as loss function during Neural Network training in PyTorch? | by Shakti Wadekar | Medium
Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names
