Neural Network Architectures: Recurrent & Sequence Models Questions
Comprehensive understanding of RNNs, LSTMs, GRUs, and Transformer architectures for sequential data. Understand the motivation for each (vanishing gradient problem, LSTM gates), attention mechanisms, self-attention, and multi-head attention. Know applications in NLP, time series, and other domains. Discuss Transformers in detail—they've revolutionized NLP and are crucial for generative AI.
MediumTechnical
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You must build a production time-series forecasting system. Compare RNN/LSTM/GRU based models to Transformer-based architectures for forecasting tasks. Discuss handling seasonality and trend, long-range dependencies, compute and latency implications, interpretability, and deployment trade-offs for each approach.
HardTechnical
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Discuss responsible AI considerations for sequence models (chatbots, summarizers) in production. Cover hallucination, biased or toxic outputs, dataset provenance and labeling practices, privacy concerns, automated detection/mitigation (toxicity classifiers, grounding with retrieval), and trade-offs between strict filtering and user experience.
HardTechnical
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As a senior ML engineer, you must choose between deploying a smaller, faster sequence model with slightly lower quality or a larger, slower model that improves an important business KPI by 4% but increases tail latency and cost. Describe how you would evaluate trade-offs, what stakeholders to involve, what experiments (A/B/canary) to run, guardrails to set, and how to choose a rollout strategy that balances product, infra, and risk concerns.
EasyTechnical
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Explain Backpropagation Through Time (BPTT) for training recurrent models. Describe how truncated BPTT works, when to use it, and how it affects model ability to learn very long-range dependencies versus training efficiency and memory footprint.
MediumTechnical
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Implement sinusoidal positional encodings in Python: given max_len and d_model return a (max_len, d_model) tensor where even indices use sine and odd indices use cosine with frequencies 1/10000^(2i/d_model). Then explain how to add these encodings to token embeddings in a PyTorch model for variable-length input batches.
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