25 Common Questions on Deep Learning

In the fields of Data Science and Artificial Intelligence, Deep Learning is one of the most in-demand skills. Deep Learning, a type of Machine Learning, finds its applications across varied Artificial Intelligence (AI) based products and services. Deep learning algorithms known as neural networks imitate the human brain and its functions and help identify and classify patterns and further draw conclusions.

If you have listed Deep Learning as one of your skills and are interviewing for a Data Science and AI role, you might be asked questions on it in the interview. Preparing interview questions on Deep Learning is a challenging task, as the topic itself is quite complex to understand. Thus, practicing interview-focused Deep Learning questions is important to successfully crack a Data Scientist or AI-related role. 

In this article, we share with you the top 25 questions on Deep Learning for Data Science and AI interviews. These questions will help you brush up on your basics and prepare for the interview. In addition to these, practicing questions on Python and SQL are quite important to crack a Data Scientist interview.

Let’s begin

Deep Learning Interview Questions

    1. What do you understand about a neural network?
    2. Define a Boltzmann Machine.
    3. Explain a gradient descent
    4. What is the need for activation functions in a neural network?
    5. Name some applications of Recurrent Neural Network.
    6. Differentiate between dropout and batch normalization.
    7. Name some data structures used in Deep Learning.
    8. How can you initialize weights in a network?
    9. Explain the different layers on CNN.
    10. Explain the working of an LSTM network.
    11. What is the use of batch normalization?
    12. What do you mean by a computational graph?
    13. Define an autoencoder.
    14. Explain the difference between valid and the same padding in CNN.
    15. Explain the concept of backpropagation.
    16. Name some disadvantages of Deep Learning.
    17. What is a sigmoid function?
    18. Name some benefits of mini-batch gradient descent. 
    19. How can you prevent overfitting?
    20. Explain the use of Fourier Transform in Deep Learning. 
    21. Define underfitting. How can it be prevented?
    22. Define a tensor.
    23. Explain the different types of a perceptron.
    24. Compare GRU with LSTM
    25. Explain the working of Pooling on CNN. 
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