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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.
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Why is the derivative of activation function all positive?
All the activation functions I see have positive derivatives.
Will negative ReLU work as well as its positive counterpart or will it lead to instability?