Sequential✓ Mathematical
◆ The PatternProcessing sequences by passing hidden state through time
RNNs process sequences step-by-step, maintaining a hidden state h that carries memory of previous inputs.
hₜ = tanh(Wₕ·hₜ₋₁ + Wₓ·xₜ + b)
hₜ = new hidden state | same weights reused at every timestep
// RNN unrolled through time
Seq length4
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Vanishing gradient: Gradients shrink exponentially over many timesteps. LSTM and GRU solve this with gated memory.
Pattern bridge: Hidden state flowing through time steps is sequential memory — like exponential moving averages where each value inherits from the past.