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

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

1. What is RNN?
2. When RNN and when CNN?
3. What are GRU?
4. What is LSTM?
5. What are forget and update gates in LSTM?
6. What is BiRNN?
7. What are word embeddings?
8. What is Beam Search?
9. What is Bleu Score?
10. What are Attention Models and how do we build them?
11. What are Auto-Encoders?
12. What is Binary Cross Entropy?
13. What are the different Classification Losses?
14. What is Negative Log Likelihood?
15. What is Margin Loss?
16. What is Soft Margin Loss?
17. What are the different Regression Losses?
18. What is L1 Loss?
19. What is MSE loss?
20. What is KL Divergence?
21. What is GAN's?
22. What are Adversarial Networks?
23. How do we do weights reuse in GAN's?
24. What is the Discriminator and Generator Loss in a GAN?
25. Why is a CNN better than Dense Layers?
26. How is the computation efficiency of CNN compared to a Simple NN?
27. How do we calculate the number of learnable parameters in the network?
28. What is Tensorboard? Why is it useful?
29. What is Grid Search?
30. Is it necessary to have the activation functions differentiable?

The Best Books recommended to improve your Machine Learning skills:

   

The links to the initial 4 parts are here:

PART 1

PART 2

PART 3

PART 4

Hope you have liked the Series on Deep Learning Interview Questions. Let me know your views in the comments below.

For Machine Learning Interview Questions check this series: Machine Learning

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