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Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The algorithm was independently derived by numerous researchers.

May 4, 2020 · Long Short-Term Memory (LSTM) are a type of neural network designed to handle long-term dependencies by handling the vanishing gradient problem. One of the fundamental techniques used to train LSTMs is Backpropagation Through Time (BPTT) where we have sequential data. In this article we summarize ho

Aug 14, 2020 · Backpropagation Through Time, or BPTT, is the application of the Backpropagation training algorithm to recurrent neural network applied to sequence data like a time series. A recurrent neural network is shown one input each timestep and predicts one output.

Aug 21, 2023 · For RNNs to learn sequential data, a variant of the backpropagation algorithm known as "Backpropagation Through Time" (BPTT) is used. In this article, we will delve into the intricate details of the BPTT algorithm and how it is used for training RNNs.

Back-propagation is the most widely used algorithm to train feed forward neural networks. The generalization of this algorithm to recurrent neural networks is called Back-propagation Through Time (BPTT).

Feb 27, 2025 · Backpropagation Through Time (BPTT) for LSTMs involves computing partial derivatives for each gate propagating gradients backward over multiple timesteps and updating weights using gradient descent. Understanding these equations helps in optimizing LSTMs for better training convergence.