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.
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.
MediumTechnical
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Implement a single-layer LSTM cell forward pass in PyTorch without using torch.nn.LSTM. Inputs: x_t (batch_size, input_size), h_prev (batch_size, hidden_size), c_prev (batch_size, hidden_size), and weight tensors W_ih, W_hh, b_ih, b_hh. Return h_t and c_t. Focus on correct gate computations and shape handling; you may assume weights are provided as torch tensors.
EasyTechnical
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Discuss tokenization strategies for NLP models: word-level, character-level, subword approaches (BPE, WordPiece, unigram), and byte-level tokenizers. For a multilingual production model, what trade-offs guide your tokenizer choice (vocabulary size, OOV handling, speed, memory)?
EasyTechnical
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Describe self-attention and how it differs from encoder-decoder attention. Explain the compute steps using queries, keys, and values (Q, K, V), how the scaled dot-product is formed, and why self-attention enables parallelization across sequence positions compared to recurrent models.
HardTechnical
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Explain gradient checkpointing (activation recomputation). How does it trade additional compute for reduced memory? For a deep transformer, describe where to place checkpoints, how to use PyTorch utilities to implement this, and how to measure runtime vs memory savings in practice.
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