Diagnose overfitting, DenseNet, preprocessing, CV

Q: Diagnose overfitting, DenseNet, preprocessing, CV

This is a Machine Learning interview question from NVIDIA for Data Scientist roles. View the full question and solution on PracHub.

Q: How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

Question

ML Interview Task: Overfitting, DenseNet vs. ResNet, Medical Imaging Pipeline, Hyperparameter Tuning, and Cross-Validation

1) Overfitting

Define overfitting.
Propose three distinct mitigation techniques and, for each one, explain:
1. Its effect on bias–variance.
2. Its impact on training dynamics.

2) DenseNet vs. ResNet

Describe DenseNet’s connectivity pattern versus ResNet.
Explain why DenseNet helps gradient flow and parameter efficiency.
Discuss scenarios where DenseNet can hurt performance or efficiency.

3) Medical Imaging Pipeline (Leakage-Free)

You are given a supervised classification problem in medical imaging (e.g., 2D X‑ray or 3D MRI). The dataset has:

Class imbalance (rare positive pathology).
Scanner/site drift (differences and temporal shifts across scanners/vendors/sites).

Detail a leakage‑free preprocessing pipeline covering normalization, augmentation, and harmonization. Justify your choices.

4) CNN Hyperparameters and Efficient Tuning

List five common CNN hyperparameters.
For each, specify a practical search space and how you would tune efficiently (e.g., schedulers, multi‑fidelity search, early stopping).

5) Cross-Validation

Compare k‑fold, stratified k‑fold, and nested cross‑validation.
State when each is necessary to avoid optimistic generalization estimates.