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
19 practiced
You need to train a Transformer-based language model on a low-resource dataset for a specialized legal domain. Outline a fine-tuning strategy using a pre-trained model, including data preprocessing, tokenization choices, learning rate schedule, regularization, and validation metrics to watch for overfitting.
MediumTechnical
20 practiced
Compare sequence models for time-series forecasting: RNN/LSTM/GRU versus Transformer-based architectures. Discuss advantages and disadvantages in terms of handling long-range dependencies, training parallelism, data efficiency, and inference latency in production.
MediumTechnical
23 practiced
Explain relative positional encodings and how they differ from absolute positional encodings in Transformers. Describe a scenario (e.g., music, code, or long documents) where relative positions provide clear benefits and why.
HardSystem Design
25 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.
MediumSystem Design
18 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.
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