Basics
← Question BankSign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark
Machine Learning
General Concepts In Machine Learning
Interview questions on General Concepts In Machine Learning.
15 questions
All subtopicsBasicsCross ValidationSimilarity MeasuresSampling Techniques and Creating Training DataFeature EngineeringBias and Variance
Sign in to bookmark
Basics
Q2. Empirical risk minimization.
- What’s the risk in empirical risk minimization?
- Why is it empirical?
- How do we minimize that risk?
Basics
Q3. What are the exhaustive steps we need to perform when tackling any generic machine learning problem, specifically for both regression and classification tasks?
Basics
Q4. Explain the tradeoff between model's flexibility vs interpretability?
Basics
Q5. Occam's razor states that when the simple explanation and complex explanation both work equally well, the simple explanation is usually correct. How do we apply this principle in ML?
Basics
Q6. If we have a wide NN and a deep NN with the same number of parameters, which one is more expressive and why?
Basics
Q7. The Universal Approximation Theorem states that a neural network with 1 hidden layer can approximate any continuous function for inputs within a specific range. Then why can’t a simple neural network reach an arbitrarily small positive error?
Basics
Q8. What are saddle points and local minima? Which are thought to cause more problems for training large NNs?
Basics
Q9. Hyper-parameters.
- What are the differences between parameters and hyper-parameters?
- Why is hyperparameter tuning important?
- List down methods for tuning hyper-parameters.
Basics
Q10. Classification vs. regression.
- What makes a classification problem different from a regression problem?
- Can a classification problem be turned into a regression problem and vice versa?
Basics
Q11. Parametric vs. non-parametric methods.
- What’s the difference between parametric methods and non-parametric methods? Give an example of each method.
- When should we use one and when should we use the other?
Basics
Q12. Why does an ML model’s performance degrade in production?
Basics
Q13. What problems might we run into when deploying large machine learning models?
Basics
Q14. Your model performs really well on the test set but poorly in production.
- What are your hypotheses about the causes?
- How do you validate whether your hypotheses are correct?
- Imagine your hypotheses about the causes are correct. What would you do to address them?
Basics