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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List practical recipes to stabilize and accelerate transformer training at scale: which optimizers and weight decays (AdamW), learning rate schedules and warmup strategies, layer normalization placement, initialization methods, gradient clipping, and tricks for large-batch training and checkpointing.
MediumTechnical
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A model's BLEU improved after switching tokenizers, but user complaints about output quality increased. Explain possible reasons tokenization changes might improve automated metrics while degrading perceived quality. Describe an investigation plan to validate the root cause and steps to remedy the issue.
MediumTechnical
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You're fine-tuning a large pre-trained transformer for a classification task with limited labeled data. Describe parameter-efficient fine-tuning strategies (adapters, LoRA, prompt tuning), layer freezing, learning rate schemes, and data augmentation techniques you would try to avoid overfitting while reducing compute and memory usage.
EasyTechnical
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Explain positional encodings used in transformer models. Compare sinusoidal positional encodings with learned positional embeddings. Discuss trade-offs such as parameter overhead, ability to extrapolate to longer sequences than trained on, and when each choice is preferable in production.
HardTechnical
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A product manager asks for an explanation of why a transformer made a particular prediction for a text input. Describe practical interpretability techniques for RNNs and transformers suitable for production: attention visualization with caveats, gradient-based saliency, Integrated Gradients, LIME/SHAP for text, perturbation/counterfactual generation, and how to present faithful explanations to non-technical stakeholders.
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