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.
HardSystem Design
0 practiced
You are tasked with deploying a distilled Transformer model for on-device mobile inference. Describe the end-to-end workflow from selecting teacher/student architectures, training the student via knowledge distillation, optimizing the model (quantization/pruning), to building a mobile runtime that meets memory and latency constraints.
MediumTechnical
0 practiced
Explain prompt engineering considerations for few-shot/zero-shot generation with large pretrained Transformers. Discuss prompt formatting, context window limits, demonstration selection, and risks like prompt injection or hallucination in production.
HardSystem Design
0 practiced
Design a continuous training pipeline for a sequence model (e.g., next-word predictor) that ingests daily data, retrains incrementally, validates for regression, and supports automated rollout. Include data validation, model versioning, canary testing, and rollback strategies.
MediumSystem Design
0 practiced
Design a sequence-to-sequence encoder-decoder architecture for machine translation using RNNs with attention. Specify encoder type, decoder type, attention variant (global/local/additive/multiplicative), training loss, and how you would handle variable-length sequences and batching for production training.
HardTechnical
0 practiced
You're observing training instability when fine-tuning a large Transformer: loss spikes and NaNs occasionally. Outline a step-by-step debugging plan to find and fix the issue. Include checks for data, optimization, numerical stability, mixed precision, and scheduler settings.
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