Vanishing gradient descent refers to a problem encountered during the training of deep neural networks, where the gradients of the loss function with respect to the parameters become very small as they propagate backward through the network. This issue is especially prevalent in networks with many layers, as the gradient diminishes exponentially with each layer. As a result, the early layers of the network receive negligible updates, leading to slower or ineffective learning. Vanishing gradients hinder the ability of the network to capture long-range dependencies and can significantly degrade the performance of deep models. Various techniques, such as careful weight initialization, activation functions, and normalization methods, have been developed to alleviate the vanishing gradient problem and improve the training of deep neural networks.
How to handle Vanishing Gradient?
Reduce model complexity.
Using ReLU Activation function.
Propper weight initialization.
Batch normalization.
Residual Network.