I got through the mechanics fine but fumbled a bit explaining why LayerNorm is preferred in Transformers.
Start by defining the core difference: batch normalization computes statistics across the batch dimension, while layer normalization computes them across the feature dimension per sample. Then explain how this affects training and inference, and discuss when to use each based on architecture and task. Finally, highlight practical tradeoffs like batch size dependence, sequence handling, and computational cost.
Pro tip: Mention that layer normalization is standard in Transformers and RNNs because it works with small batches and variable sequence lengths, while batch normalization is common in CNNs for vision tasks where large batches are feasible. This shows you understand real-world deployment constraints.
Explain that batch norm normalizes each feature across the batch (using batch mean/variance), while layer norm normalizes each sample across its features (using per-sample mean/variance).
Note that batch norm uses batch statistics during training but running averages at inference, whereas layer norm uses the same computation at both times, making it more consistent.
Batch norm is common in CNNs for image tasks (e.g., ResNet) where large batches are used; layer norm is standard in RNNs and Transformers (e.g., BERT, GPT) due to variable sequence lengths and small batch sizes.
Batch norm depends on batch size and can be unstable with small batches; layer norm is batch-independent but may be less effective for convolutional layers. Also mention computational overhead and memory differences.
Relate to Snapchat's context: for image/video models, batch norm might be used; for text or sequence models, layer norm is preferred. Emphasize choosing based on data modality and deployment constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining calibration clearly: a model is well-calibrated if its predicted probabilities match the actual observed frequencies. Then outline a practical workflow: check calibration with reliability diagrams and metrics like ECE, and fix it using post-processing methods such as Platt scaling or isotonic regression, or by retraining with a proper scoring rule.
Pro tip: Emphasize that calibration is critical when probabilities drive decisions (e.g., ad bidding or content ranking at Snapchat), and mention that you'd validate calibration on a held-out set and monitor it in production, as calibration can drift over time.
Explain that a well-calibrated model's predicted probabilities reflect true likelihoods: among all predictions of 0.8, about 80% should be positive.
Use reliability diagrams (calibration curves) and quantitative metrics like Expected Calibration Error (ECE) or Brier score on a validation set.
Identify why miscalibration occurs: model bias, overfitting, class imbalance, or using loss functions that don't optimize probabilities (e.g., hinge loss).
Use post-processing methods like Platt scaling (sigmoid) or isotonic regression, or retrain with a proper scoring rule (e.g., log loss) and more data.
Re-evaluate calibration on a held-out set and set up production monitoring to detect drift, retraining or recalibrating as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered class weighting, resampling, focal loss.
Start by clarifying the business context and the cost of different errors, then walk through a structured pipeline: data-level or algorithm-level training strategies, evaluation metrics that are robust to imbalance, and threshold selection based on the precision-recall trade-off. Emphasize that the right approach depends on the specific problem and that you'd validate choices with experiments.
Pro tip: At Snapchat, many problems (e.g., spam detection, friend suggestions) have severe class imbalance, so mention that you'd first check if the imbalance is extreme enough to warrant special handling—sometimes a simple class weight adjustment suffices. Also, always tie threshold selection to a concrete business metric like expected cost or user engagement.
Ask about the business objective, the cost of false positives vs. false negatives, and the current class distribution. Explore the data to see if the imbalance is due to a rare event or a sampling artifact.
Consider data-level approaches (oversampling, undersampling, SMOTE) or algorithm-level approaches (class weights, focal loss). Explain that you'd start with the simplest method (e.g., class weights) and only move to more complex ones if needed.
Avoid accuracy; use precision, recall, F1, AUC-ROC, and especially AUC-PR (precision-recall curve) for imbalanced data. Also consider business-specific metrics like lift or expected cost.
Use the precision-recall curve to choose a threshold that balances precision and recall according to the business cost. If costs are known, compute the expected cost at each threshold and pick the minimum.
Use cross-validation with stratification, and if possible, test on a holdout set that reflects the real-world distribution. Monitor performance over time and be ready to adjust as data drifts.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.