RNN, LSTM, GRU — and why we left them

Interview questions to prep

1. Compare DFS, BFS, and Union-Find — pick one and defend it.
2. What if the grid is huge and streamed (rows arrive one at a time)?

Interview questions to prep

1. Is this BFS, DFS, or Union-Find? Defend the choice over the other two.
2. Walk through complexity in terms of V and E. Where do those costs come from?
3. How would you handle disconnected components, self-loops, or duplicate edges?

Interview questions to prep

1. Why do vanilla RNNs struggle with long-term dependencies?
2. Walk through truncated backprop-through-time — why is it the practical default?
3. What causes exploding gradients in BPTT, and where do clipping or normalization help?
4. How would you set up next-word prediction with an RNN, and what is the target at each time step?

Interview questions to prep

1. Walk through an LSTM cell: forget, input, output gates and cell state.
2. How does the cell state help with vanishing gradients?
3. Why is a BiLSTM useful for sequence labeling but not valid for causal next-token generation?

Interview questions to prep

1. When would you pick GRU over LSTM?
2. What does GRU's update gate do that LSTM splits across forget + input gates?