New answers tagged backpropagation
0
I really liked the question. Yes, we sum over derivatives. First of all think what backpropagation is trying to do: finding the affect of each parameter on the loss.
So as you said:
the same filter is used multiple times on the input while convolving
meaning that each kernel affects the final loss in several ways, so those affects should be summed together,...
0
The claim that Neural Network with a single hidden layer can model any functions is proven in Cybenko's Approximation by superpositions of a sigmoidal function.
https://link.springer.com/article/10.1007/BF02551274
check also: https://en.wikipedia.org/wiki/Universal_approximation_theorem
The thing is that the neural network using sigmoidal functions, which ...
-1
Don't know if you have this doubt anymore, but this would be helpful for those who are facing similar problems-
You will need to find the correct weights with which you add these two loses by hyperparameter search. That is, find the best $\lambda$ for the loss-
$$
L = Loss_1 + \lambda(Loss_2)
$$
Here $Loss_1$ and $Loss_2$ can be any losses. Here, we take ...
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