Most Common Interview questions for a Deep Learning Engineer - PART 2/5

These are some of the most common interview questions for Deep Learning Engineer
PART 2/5

1. How do we overcome underfitting and overfitting?
2. Does more data solve high variance or bias problem?
3. What is Regularization?
4. What are the types of regularisation?
5. What is the learning rate in NN?
6. is it better to have a high or low learning rate?
7. Does increasing the learning rate get you results faster?
8. What are dropouts?
9. What do we do with dropouts in Training/Testing phase?
10. Can you give an intuition of dropouts?
11. What is data augmentation?
12. What are the different types of Data Augmentation?
13. What is Early stopping? How do we achieve that?
14. What framework is better Tensorflow/Pytorch/any other or better to code it yourself as it have better control over the results?
15. What is gradient checking? How do we do that?
16. Is it required to Normalize the training set ? if so why and explain the process.?
17. Is it required to Normalise the Test set? If so why and explain the process.
18. What is vanishing and exploding gradients?
19. What are the Optimization Algorithms in ML?
20. What is Batch Gradient Descent?
21. What is Mini-Batch Gradient Descent?
22. What is Stochastic Gradient Descent?
23. What is Gradient Descent with Momentum? What are the hyperparameters involved in it? Explain?
24. What is RMSProp? What are the hyperparameters involved in it?
25. What is Adam Optimization?
26. What is Adagrad?
27. What is Learning Rate Decay?
28. What is a saddle point in Loss landscape?
29. How does Adam Optimization help?
30. What are the different hyperparameters involved in a NN?

The Best Books recommended to improve your Machine Learning skills:

   

For Part 3 of the series click here: PART 3