regularization

Fine-tune a model on your domain and it can forget how to do everything else. The signal is explaining why shared weights cause it and naming the concrete mitigations that all reduce to one principle.

Dropout is five lines, yet most candidates botch two of them: disabling it at inference and scaling the survivors so the expected activation stays constant. Inverted dropout is the convention they want to see.

A near-universal ML fundamentals question. Anyone can say 'L1 is lasso, L2 is ridge'; the signal is the gradient-and-geometry reason L1 drives weights to exactly zero and when you'd pick each. Here is that answer.

Feature selection cuts noise, overfitting, and serving cost, but the three method families pull in different directions. The signal interviewers want is filter vs wrapper vs embedded and the conditions under which you reach for each.

Dropout is the classic neural-net regularizer, but the signal is whether you can explain why zeroing activations forces redundancy, and the train-vs-inference scaling bug that trips most candidates. Here is the answer.

Data augmentation cheaply expands training data and regularizes models, but valid transforms differ by modality. The signal is the label-preserving constraint and why text is the hard one. Here is the answer.

Two cheap regularizers that fix overconfident classifiers. The signal is knowing that one softens the target and the other softens the input, and exactly why softer signals calibrate the model. Here is the answer.

EM on a Gaussian mixture has a famous failure mode: a Gaussian collapses onto one point and likelihood shoots to infinity. Knowing why, and the three standard fixes, separates people who used sklearn from people who understand it.

Lasso gives sparsity, Ridge handles correlated features, and each fails where the other shines. Elastic Net blends them for a specific, common failure. Here is when it earns its two hyperparameters.