The vanishing gradient problem occurs when the gradients of the loss function become very small during backpropagation, especially in deep neural networks. This issue is most common with certain activation functions like sigmoid or tanh, where the gradients approach zero for large inputs. When this happens, the weights of the earlier layers in the network receive very small updates, leading to slow or stagnant learning.
This problem becomes particularly significant in deep networks with many layers, as the gradients diminish exponentially as they propagate backward. This can prevent the network from learning effectively, especially in the initial layers.
Solutions to the vanishing gradient problem include using activation functions like ReLU, which are less prone to gradient vanishing, and techniques like batch normalization or weight initialization methods like Xavier or He initialization, which help maintain gradient magnitudes during training.