Backpropagation Through Time (BPTT)

Backpropagation Through Time (BPTT) is a training technique for recurrent neural networks that handle sequential data. It unrolls the network across each step of the sequence, treating it like a longer, layered network. During training, BPTT calculates errors (differences from desired outputs) at each step and then propagates these errors backward through the entire sequence. This process updates the network's internal parameters (weights), helping it learn patterns over time. Essentially, BPTT allows the network to understand how earlier inputs influence later outputs by considering the sequence's temporal dependencies.