rnn
AI, ML & GenAI 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 walk through a sequence one position at a time via a hidden state, an approach that is principled but slow and weak on long-range dependencies because gradients shrink across many steps. Gates in LSTMs and GRUs carry information further, and seq2seq encoder-decoder models with attention broke the single-vector bottleneck, the idea transformers later pushed all the way. AI, ML, and GenAI engineer interviews probe this because it explains where attention came from and why the field traded 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 drive it toward zero (early layers stop learning) and factors above one make it explode (training diverges into NaNs). The root cause is that repeated multiplication, and the standard fixes attack it head-on: residual connections to hand gradient a shortcut, normalization to keep activations in scale, gating to hold signal across time, gradient clipping to cap the blow-up, and careful initialization. AI, ML, and GenAI engineer interviews probe it because it is the mechanism behind most deep-net training failures you have to diagnose.📊 Evaluation & ML FoundationsSign in