List regularization methods and trade-offs

Q: List regularization methods and trade-offs

This question evaluates competency in machine learning regularization and model generalization, covering familiarity with methods such as L1/L2, dropout, early stopping, data augmentation, label smoothing, mixup, normalization, Bayesian priors, and ensembling.

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Question: Compare Regularization Techniques and When to Use Them

Context: You are interviewing for a machine learning engineering role and are asked to explain the landscape of regularization techniques: what they are, when to use them, and their effects.

Tasks:

List and compare the following regularization techniques:
- L1 vs L2 (weight decay)
- Dropout
- Early stopping
- Data augmentation
- Label smoothing
- Mixup
- Normalization as an implicit regularizer (e.g., BatchNorm, LayerNorm)
- Bayesian priors
- Ensembling
For each, explain:
- When you would use it
- Effect on bias–variance
- Effect on sparsity
- Impact on training dynamics
- Typical pitfalls
Provide intuition, small examples/formulas where useful, and any practical guardrails for choosing and validating these techniques.

List regularization methods and trade-offs

Question: Compare Regularization Techniques and When to Use Them

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List regularization methods and trade-offs

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Question: Compare Regularization Techniques and When to Use Them

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