What is the vanishing gradient problem and how is it addressed?

The vanishing gradient problem is a phenomenon that can occur during the training of neural networks, particularly deep neural networks. It refers to the situation where the gradients used to update the weights of the neural network become extremely small, effectively disappearing, as they propagate backward through the network. This can lead to the weights not being updated, or being updated so slowly that the network fails to learn effectively.

One common solution to the vanishing gradient problem is the use of activation functions that have steeper derivatives, such as the rectified linear unit (ReLU) function. Another approach is to use a variant of the gradient descent algorithm called stochastic gradient descent with momentum, which allows the gradient updates to accumulate over time, reducing the impact of small gradients. Additionally, techniques such as batch normalization can be used to rescale the inputs to each layer, which can help prevent the gradients from becoming too small. Finally, more recent architectures such as residual networks and highway networks have been designed specifically to alleviate the vanishing gradient problem in deep neural networks.