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🔄 RNN Variants: GRU, Bidirectional, Deep RNNs
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🔄 RNN Variants: GRU, Bidirectional, Deep RNNs
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Now · 1. 🎯 Learning Objectives
🔄 RNN Variants: GRU, Bidirectional, Deep RNNs
1. 🎯 Learning Objectives
- Explain GRU: reset gate, update gate
- Describe bidirectional RNN for full context
- Stack RNN layers for deeper representations
2. 📖 Core Content
3.1 GRU
z_t = σ(W_z·[h_{t-1}, x_t]) (update gate) r_t = σ(W_r·[h_{t-1}, x_t]) (reset gate) h̃_t = tanh(W·[r_t⊙h_{t-1}, x_t]) h_t = (1-z_t)⊙h_{t-1} + z_t⊙h̃_t
3.2 Bidirectional RNN
Forward: h→_t, Backward: h←_t. Output: h_t = [h→_t; h←_t]
3.3 Deep (Stacked) RNN
Multiple RNN layers on top of each other. Each layer processes the previous layer's hidden states.
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