Categorical loss vs Sparse Categorical loss

while defining losses,when to use sparse categorical loss and when to use categorical loss. kindly explain in a layman language as am new to this.if possible with an example.

Kindly have a look into this blog post for explanation about when to use sparse categorical loss and categorical loss.

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Essentially, for categorical loss, you pass the ground truth as one-hot encodings, and for Sparse Categorical loss, you pass direct class indices as ground truth.

The above is with reference to Keras/TF.
In PyTorch, the CrossEntropyLoss by default works in sparse ground truth mode.

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