Why Sigmoid Fails in Deep Neural Network?
Deep neural networks rely on the backpropagation algorithm to train effectively. A crucial component of backpropagation is the calculation of gradients, which dictate how much the weights in the network should be updated. However, when the sigmoid activation function is used in hidden layers, it can lead to the vanishing gradient problem. In this post, we’ll focus on how gradients are calculated and why sigmoid makes them vanish in deep networks. ...