Backpropagation Through Time

This animation visually walks through backpropagation through time in a recurrent neural network, tracing how gradients flow backward through three unrolled time steps. It highlights how the gradient for the weight matrix W is the sum of contributions from each step, and contrasts scenarios where these gradients vanish (shrink) or explode (grow) over time.