As per my knowledge, in back propagation- loss function or gradient is used to update the weights. in back propagation, weights became small w.r.t gradients, this leads to vanishing gradient problem.
can you please give insights about these two terms (gradient(SGD), exploding gradient problem, vanishing gradient problem).
how to select which activation function is useful/suitable at different layers?