Vanishing gradient problem
The vanishing gradient problem is a challenge encountered in the training of Artificial neural networks, where the gradients used to update the network's weights become smaller as they are backpropagated through the layers, making it difficult for the network to learn.
This problem arises because the gradients are multiplied together during backpropagation, and when these gradients are small, the product becomes even smaller, leading to vanishing gradients. This issue is particularly pronounced in deep neural networks, where the gradients have to be backpropagated through many layers.
To address the vanishing gradient problem, various techniques have been developed, including the use of Activation functions such as Rectified linear unit (ReLU) and Long short-term memory (LSTM) networks, which are designed to mitigate the effects of vanishing gradients. Additionally, Batch normalization and Gradient clipping can also help to alleviate this problem.