The vanishing gradient problem is a problem when training deep neural networks using gradient-based methods such as backpropagation. Recall in previous chapters, we discussed the backpropagation algorithm in training neural networks. In particular, the loss function provides information on the accuracy of our predictions, and allows us to adjust the weights in each layer, to reduce the loss.
So far, we have assumed that backpropagation works perfectly. Unfortunately, that is not true. When the loss is propagated backward, the loss tends to decrease with each successive layer:
As a result, by the time the loss ...
