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A Recurrent Neural Network (RNN) processes sequences by carrying a hidden state that is updated at every time step using h_t = f(W_h h_{t-1} + W_x x_t + b).
Gradient clipping limits how large gradient values or their overall magnitude can become during optimization to prevent exploding updates.