The Vanishing Gradient Problem

- Overview

The vanishing gradient problem refers to the issue of diminishing gradients during the training of deep neural networks. It occurs when the gradients propagated backward through the layers become very small, making it difficult for the network to update the weights effectively.

The vanishing gradient problem is a challenge that occurs when training neural networks, especially deep feedforward and recurrent neural networks. 

It happens when the gradients used to update the network weights become very small as they are passed back through the network. This can make it difficult for the network to learn and can even stop it from training further.

Here are some reasons why the vanishing gradient problem occurs:

• Activation functions: The choice of activation functions, such as sigmoid and hyperbolic tangent (tanh), can cause the problem. These functions limit the range of input values, which makes their derivatives small.

• Backpropagation: During backpropagation, gradients are calculated using the chain rule and passed back through the network from the output layer to the input layer.

• Sequence length: As the sequence length increases, the gradient magnitude typically decreases.

One way to help the network learn better is to use skip connections, which allow the gradient signal to flow more easily through the network.