If each layer of the neural network uses the Sigmoid activation function and the number of layers is large, which problem may occur?

When using the Sigmoid activation function in a neural network with a large number of layers, the vanishing gradient problem is likely to occur. The Sigmoid function has a characteristic "S" shape and outputs values between 0 and 1. When the output of neurons approaches these asymptotes, the gradient (or the derivative) of the Sigmoid function becomes very small.

As backpropagation algorithms propagate the error gradients backward through the layers, these small gradients can effectively vanish, especially in the earlier layers of the network. As a result, weight updates in those layers become negligible, leading to slow learning or even halting of learning entirely. Thus, even with a complex architecture, the model may struggle to capture the underlying patterns in the data effectively.

This situation is in contrast to concepts like overfitting, where a model is too complex for the data and performs poorly on unseen data; or underfitting, where the model is too simple to capture the data patterns. While gradient explosion may occur under different circumstances, particularly with certain types of activation functions or architectures, it is not the primary concern with the Sigmoid activation function in deep networks.