Recurrent Neural Network Quiz

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Core recurrence

h_t = tanh(W_xh x_t + W_hh h_(t-1) + b_h)
y_t = W_hy h_t + b_y

Simple RNN parameters

I × H + H × H + H

I is input size. H is hidden size. The last H is one bias vector.

Common batch-first shapes

Input: (batch, timesteps, features)
Full output: (batch, timesteps, hidden)
Final output: (batch, hidden)
Bidirectional concatenation: hidden × 2

Gradient controls

Gradient clipping controls exploding gradients.
Gated units help preserve long-term dependencies.
Truncated BPTT limits gradient history.

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Recurrent Neural Network Quiz

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