Deep Learning Interview Questions and Answers

Deep Learning Interview Questions and Answers

Deep Learning Interview Questions and Answers

Deep Learning Interview Questions and Answers for beginners and experts. List of frequently asked Deep Learning Questions with answers by Besant Technologies. We hope these Deep Learning interview questions and answers are useful and will help you to get the best job in the networking industry. These Deep Learning interview questions and answers are prepared by Deep Learning Professionals based on MNC Company’s expectations. Stay tuned we will update New Deep Learning Interview questions with Answers Frequently.

Besant Technologies supports the students by providing Deep Learning interview questions and answers for the job placements and job purposes. Deep Learning is the leading important course in the present situation because more job openings and the high salary pay for this Deep Learning and more related jobs.

Best Deep Learning Interview Questions and Answers

Here is the list of most frequently asked Deep Learning Interview Questions and Answers in technical interviews. These Deep Learning questions and answers are suitable for both freshers and experienced professionals at any level. The Deep Learning questions are for intermediate to somewhat advanced Deep Learning professionals, but even if you are just a beginner or fresher you should be able to understand the Deep Learning answers and explanations here we give.

In this post, you will get the most important and top Deep Learning Interview Questions and Answers, which will be very helpful and useful to those who are preparing for jobs.

Q1. What about Deep Learning?

Q2. What is the difference between a deep network and shallow network?

Q3. What is the cost function in Deep Learning?

Q4. What is the importance of the cost function?

Q5. What is backpropagation?

Q6. What is gradient descent?

Q7. What are the types of gradient descent?

Q8. What do you understand by the term Multi-Layer Perceptron (MLP)?

Q9. What is data normalization? When do you use it?

Q10. Why do we use activation function in a neural network?

Q11. What is the primary role of the activation function?

Q12. What is the significance of Weight Initialization in Neural Networks?

Q13. What is the main difference between Deep learning, Machine learning, and AI?

Q14. Define Hperparameteres and give a few examples

Q15. What is the dropout?

Q16. Does dropout prevent the overfitting? If yes, then how?

Q17. Write down a few names of deep learning frameworks?

Q18. What are tensors?

Q19. What are the benefits of using tensors?

Q20. What do you learn by the computational graph?

Q21. Describe the main layers of the CNN

Q22. Define the concept of RNN?

Q23. What are the most common issues faced with RNN?

Q24. Explain the primary importance of LSTM?

Q25. What is an autoencoder?

Q26. What are the principles layers in the autoencoder?

Q27. What are the variations in autoencoder?

Q28. Are there any limitations of Deep learning?

Q29. Is it difficult to learn about deep learning?

Q30. Describe what do you understand by CNN?

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