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About this interview
A technical interview on Sequence Models, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain recurrent networks and backpropagation through time including the vanishing gradient problem
Describe how LSTM gating mechanisms address long-range dependency limitations of vanilla RNNs
Explain seq2seq with attention and the connection to the Transformer architecture
Distinguish when to use RNN-based models vs Transformer-based models for sequential data
Topics covered
RNN FundamentalsBPTT & GradientsLSTM ArchitectureGRUBidirectional & Deep RNNsSeq2SeqAttention MechanismAttention to TransformerModel SelectionPractical Implementation
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“At a high level, what makes recurrent neural networks different from feedforward networks, and what problem were they designed to solve?
“Explain mechanically why the LSTM architecture is less susceptible to the vanishing gradient problem compared to a vanilla RNN.
“How does the addition of attention to a seq2seq model address the information bottleneck problem, and what does the context vector represent at each decoder step?