Backpropagation Through Time

Backpropagation Through Time (BPTT) is a method used to train recurrent neural networks (RNNs), which process sequences of data, like sentences or time series. It involves running the network forward to make predictions, then calculating the errors between predictions and actual outcomes. BPTT then "unwraps" the network through time, tracing those errors back through all previous time steps to adjust the model's weights. This helps the network learn from past mistakes across the entire sequence, improving its understanding and performance on tasks involving sequential information.