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Top 20 Deep Learning Interview Questions with detailed Answers (All free)

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Deep Learning Interview Questions

  1. What is a Perceptron? Discuss its various components
  2. What is a Multilayer Perceptron (MLP), also commonly known as Feed Forward Neural Network?
  3. What do you mean by pretraining, finetuning and transfer learning?
  4. Describe the training process of a Neural Network model (Forward and Backward propagation)
  5. What are the key hyper-parameters of a neural network model?
  6. What is an activation function, and what are the common choices for activation functions?
  7. What are some options to address overfitting in Neural Networks?
  8. Why do we add bias component in a perceptron / neural network?
  9. What is Rectified Linear Unit (ReLU) activation function? Discuss its advantages and disadvantages
  10. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
  11. What is the vanishing and exploding gradient problem, and how are they typically addressed?
  12. What do you mean by saturation in neural network training? Discuss the problems associated with it
  13. Discuss Softmax activation function
  14. How does dropout work?
  15. Explain the Transformer Architecture
  16. What are the primary advantages of transformer models?
  17. What are the limitations of transformer models?
  18. Explain Self-Attention, and Masked Self-Attention as used in Transformers
  19. What are recurrent neural networks (RNNs), and what are their applications?
  20. What is Long-Short Term Memory (LSTM)?

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