Top 20 Deep Learning Interview Questions with detailed Answers

Categories: Deep Learning
Updated: September 7, 2026
Top 20 Deep Learning Interview Questions

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. Multilayer Perceptrons: Task-Specific Design and Self-Attention
  4. What do you mean by pretraining, finetuning and transfer learning?
  5. Describe the training process of a Neural Network model (Forward and Backward propagation)
  6. Explain the Transformer Architecture
  7. What are the primary advantages of transformer models?
  8. What are the limitations of transformer models?
  9. How Do Residual Connections Help Neural Network Training?
  10. What are some options to address overfitting in Neural Networks?
  11. Explain Gradient Descent and Its Variants
  12. How is LoRA different from Adapters? And when to use which?
  13. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
  14. What is the vanishing and exploding gradient problem, and how are they typically addressed?
  15. How does dropout work?
  16. What is AutoGrad?
  17. Explain Self-Attention, and Masked Self-Attention as used in Transformers
  18. How Learning Rate Scheduling Works (with PyTorch Examples)
  19. What are recurrent neural networks (RNNs), and what are their applications?
  20. What is Long-Short Term Memory (LSTM)?

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