Most Common Interview questions for a Deep Learning Engineer - PART 3/5

These are some of the most common interview questions for Deep Learning Engineer
PART 3/5

1. How do we go with selecting these hyperparameters?
2. What is Batch Normalization?
3. What are the parameters involved in Batch Normalization?
4. Are there any learnable parameters in Batch Normalization step?
5. Why Batch Normalization?
6. What is covariance shift?
7. How do we do Batch Normalization at Train and Test time?
8. What are the benefits of Batch Normalization?
9. What is Multiclass Classification?
10. What is a Sigmoid Layer?
11. What is a Softmax function?
12. When do we do softmax or sigmoid activation?
13. What is Cross Entropy?
14. What is L1 regularization?
15. What is L2 regularization?
16. Which is better L1 or L2 regularization?
17. What is precision/Recall/F1 score? Explain with an example?
18. How do we do Error Analysis in ML?
19. What is Ceiling Analysis?
20. What is Transfer Learning?
21. What is Fine Tuning?
22. What is Multitask learning? How do we do that?
23. What is End-to-End Deep Learning?
24. What is CNN?
25. What is Translation Invariance?
26. What is Convolution operation, explain with example?
27. What is Edge detection?
28. What is a Sobel operator?
29. What is padding?
30. What is Valid/Same padding? Which is better?

The Best Books recommended to improve your Machine Learning skills:

   

For Part 4 of the series click here: PART 4