mlds fund

Most candidates recite the Adam update equation and stop. The interviewer wants the decision: when plain SGD with momentum still wins, why Adam is the safe default for new architectures, and how you diagnose a training run that is silently dying from a gradient that shrank to zero.

Backprop is just the chain rule run efficiently in reverse, but the question that separates levels is why a hundred-layer network trains when a naive one wouldn't. The credit-assignment intuition plus the three tricks that keep gradients alive.

The answer is not just 'transformers are better.' It is two specific wins: every position can attend to every other in one step (long-range dependencies) and the whole sequence trains in parallel instead of one token at a time. Plus the cost that buys, and where RNNs still make sense.

Convolution and parameter sharing are why a CNN needs a tiny fraction of the weights a dense net would, and why it generalizes across position. The conceptual answer, the feature-hierarchy picture, and the honest take on when a fine-tuned CNN still beats reaching for a giant vision-language model.

Both are tree ensembles, but one averages independent deep trees to kill variance and the other adds shallow trees in sequence to kill bias. That single difference dictates the failure modes, the tuning burden, and which one you hand a customer who will retrain it after you leave.

The margin idea is elegant and worth understanding, and the kernel trick is a clever move. But the question that separates levels is why a model that dominated the 2000s is now a niche pick. The scaling math that killed it for big data, and where it still wins.

The base-rate trap is the classic, and it is not academic: it is exactly how a high-accuracy fraud or disease classifier drowns ops in false alarms. The worked numbers, the tree you draw on the whiteboard, and the two levers that actually fix it in production.

MAP is MLE plus a prior, and that prior is not academic decoration: it is the same thing as regularization, and it is what stops a model from making confident nonsense out of three data points. The connection that makes this an applied answer, not a stats-class recitation.

Picking the wrong distribution silently breaks your model: counts are not Gaussian, and treating them that way predicts negative events and underestimates the variance of busy periods. The decision tree by data type, the variance gotcha, and what each choice implies for the model you reach for.

The honest version: deep learning learns its own features but is hungry for data and compute, and on a customer's 50,000-row tabular dataset a gradient-boosted tree wins on accuracy, cost, and time-to-ship. The dividing line and the conditions that flip it.

A Type I error blocks a real invoice; a Type II error delivers the phishing email. Which one costs the customer more decides where you set the threshold. The spam-filter framing, the precision/recall mapping, and the asymmetric-cost call interviewers want.

Profile before you model: shape, types, target balance, leakage check. Then the part most candidates botch, that a missing value is often the most predictive feature in the table, so you flag it before you fill it. The first-hour playbook and the imputation decisions that survive production.

Triplet loss contrasts an anchor against one negative; modern contrastive losses like InfoNCE contrast against a whole batch at once, which is why CLIP-style training scales and old triplet pipelines stalled. The mining problem, the batch-size lever, and the call interviewers want.

L2 shrinks every weight smoothly toward zero; L1 drives many weights to exactly zero, which is automatic feature selection. The geometric reason L1 hits the corners is the part interviewers actually want, plus the call on when sparsity beats smooth shrinkage.

A confusion matrix is the 2x2 of right and wrong predictions per class, and it is the reason a 99% accurate fraud model can be useless. The worked imbalance example, and how reading the matrix as precision and recall fixes the lie.

The validation set is the one you tune and select on; the test set is the one you only look at once. Confusing them, or tuning so hard you overfit the validation set, is how a model that aced offline metrics dies in production.

Gradient descent is walking downhill on the loss surface, one step proportional to the slope. Knowing when to stop is the part that separates people who have trained models from people who have read about them: it is the validation curve, not the training curve, that tells you.

Thirty percent disagreement is not a fact about the annotators. It is a fact about the task definition, and on a skewed label set it can mean the labels are worse than chance. The week is spent on the guideline and the gold set, not on the model, because no model trains past the noise in its labels.