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For questions about the back-propagation (aka "backprop", and often abbreviated as "BP") algorithm, which is used to compute the gradient of the objective function (e.g. the mean squared error) with respect to the parameters (or weights) of the neural network, when trained with gradient descent.

1 vote
1 answer
694 views

How to compute the gradient of the cross-entropy loss function with respect to the parameter...

I've seen plenty of examples of people doing Sigmoid + MSE backpropagation implementations, yet I do not seem to understand how to implement backpropagation as stated in the title in the case of multi-class …
Ilknur Mustafa's user avatar
0 votes
1 answer
383 views

Why is the derivative of the softmax layer shaped differently than the derivative of other n...

If the derivative is supposed to give the rate of change of a function at that point, then why is the derivative of the softmax layer (a vector) the Jacobian matrix, which has a different shape than t …
Ilknur Mustafa's user avatar