rnn
Applied AI interview questions tagged rnn, across every topic.
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Concepts behind "rnn"
The curriculum that explains the ideas these questions test.
Foundational
From RNNs to Transformers: RNN, LSTM, Seq2SeqRecurrent networks process sequences one step at a time through a hidden state, which makes them principled but slow and bad at long-range dependencies because gradients vanish across many steps. LSTMs and GRUs add gates to carry information further, and seq2seq encoder-decoder models with attention removed the single-vector bottleneck, which is the idea transformers then took to its conclusion. Applied-AI interviews probe this because it explains why attention exists and why we abandoned recurrence for parallelism.🧠 Foundations of LLMs & GenAI
Core
Vanishing and Exploding GradientsIn a deep or recurrent network the backward gradient is a product of many per-layer Jacobians, so its magnitude compounds: factors mostly below one shrink it toward zero (early layers stop learning) and factors above one blow it up (training diverges into NaNs). The root cause is the repeated multiplication, and the standard fixes target it directly: residual connections to give gradient a shortcut, normalization to keep activations in scale, gating to preserve signal across time, gradient clipping to cap the blow-up, and careful initialization. Applied-AI interviews probe it because it is the mechanism behind most deep-net training failures you have to diagnose.📊 Evaluation & ML FoundationsSign in