Most Common Interview questions for a Deep Learning Engineer - PART 1/5

These are some of the most common interview questions for Deep Learning Engineer
PART 1/5

1. What is a Linear Regression? Answer
2. What is Logistic Regression?
3. What is the Cost function used for a Regression problem?
4. What is the cost function used for a Classification Problem?
5. What is a Neural Network?
6. What is Computational Graph?
7. What is Forward Pass and Backward Pass in a NN?
8. What is Gradient Descent?
9. What is a rank 1 in a numpy array?
10. What are the activation functions?
11. Name and explain different activation functions.
12. Why do we need non-linearity in a network?
13. Why is tanh better than sigmoid?
14. Why is relu better than others?
15. What is relu activation?
16. What is leaky relu? 
17. How do you write a relu/leaky relu function?
18. How do we initialize the weights in a network?
19. what is Xavier initialization?
20. Why "Deep" Neural Networks?
21. How do we proceed with an ML workflow?
22. How do we split the data in ML workflow?
23. What is cross-validation?
24. Is it necessary to have Dev and Test set form the same distribution?
25. What is Bias and Variance in ML?
26. Is good to have High Bias or High Variance :)
27. How do we overcome High Bias?
28. How do we overcome High Variance?
29. What is the Optimal Bayes Error?
30. Is underfitting High/Low - Bias or Variance Situation?

The Best Books recommended to improve your Machine Learning skills:

   

For Part 2 of the series click here: PART 2