Vanishing Gradient Problem

The Vanishing Gradient Problem occurs in training deep neural networks, where the essential updates used to improve the model become extremely small as they are propagated back through many layers. Think of it like trying to pass a message through a long line of people; by the time the message reaches the end, it may be barely understandable. Similarly, small gradients make learning slow or even halt in early layers, preventing the network from effectively learning complex patterns. This challenge can hinder the training process and limit the network’s ability to realize its full potential.