adjusted due to extremely small gradient and that's the vanishing gradient problem let's see how this applies to recurrent neural networks you can think of each time step and over current no network as a layer to train a recurrent neural network use an application of backpropagation called back propagation through time the gradients value will exponentially shrink as it propagates for each time step again the gradient is used to make the adjustments in the neural networks weights thus allowing it to learn small gradients means small