deep learning

Backprop is the algorithm that makes deep learning trainable, and the interviewer wants the chain-rule mechanics, not just 'it computes gradients.' The signal is the forward-then-backward flow and why it is efficient. Here is the answer.

BatchNorm is one of the most-asked deep-learning questions, and the trap is the train/inference difference. The signal is what it normalizes, why it stabilizes and speeds training, and why it switches to running statistics at inference. Here is that answer.

This question ties together why deep nets were hard to train and the cluster of tricks that fixed it. The signal is the multiplicative-gradient cause and naming the real fixes: ReLU, He/Xavier init, residuals, normalization. Here is the answer.

A staple that checks whether you know why ReLU dethroned sigmoid and how to match the output activation to the loss. The signal is the vanishing-gradient story told correctly. Here is the answer.

Bad initialization makes deep nets fail to train at all, with activations that vanish or explode through depth. The signal is knowing why zero init is fatal and how to match Xavier or He to your activation function.

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.

Classic bias-variance says bigger models eventually overfit, yet deep nets keep improving past the point where they memorize the data. The signal is explaining the second descent and why over-parameterized models generalize. Here is the answer.

A million rows does not automatically mean deep learning. The signal is reasoning from dimensionality, data type, inference latency, and label budget, then noting where KNN quietly survives as approximate nearest neighbor. Here is the answer.

Raw gradient saliency maps are noisy and saturate. Integrated gradients fixes both with two axioms and a path integral, but the baseline choice quietly decides the answer. Here is what a careful candidate explains.