1. What is the difference between deep learning and traditional machine learning?
2. How do convolutional neural networks (CNNs) process image data?
3. What is transfer learning, and when is it useful?
4. How do recurrent neural networks (RNNs) handle sequential data?
5. What are attention mechanisms, and how do they improve neural networks?
6. Explain the concept of the loss function in training AI models.
7. What is regularization, and why is it important in machine learning?
8. How does gradient descent work in optimization?
9. What is the vanishing gradient problem in deep learning?
10. How can you prevent overfitting in a model?
11. What is the role of dropout in neural networks?
12. How do generative adversarial networks (GANs) work?
13. What are the main components of reinforcement learning?
14. How do you define and measure success in an AI project?
15. What is feature extraction, and why is it necessary?
16. How does the K-means clustering algorithm function?
17. What is the purpose of dimensionality reduction techniques?
18. How does natural language processing differ from traditional linguistics?
19. What are word embeddings, and how do they enhance NLP?
20. Explain the concept of a recurrent neural network's hidden state.
21. What is the significance of the learning rate in training neural networks?
22. How do you evaluate the performance of a classification model?
23. What is cross-entropy loss, and when is it used?
24. What are the challenges of implementing AI in healthcare?
25. How do ensemble methods like random forests improve model performance?
26. What are the advantages of using deep reinforcement learning?
27. How does the support vector machine algorithm classify data?
28. What is the difference between L1 and L2 regularization?
29. How can you interpret the results of a confusion matrix?
30. What role do hyperparameters play in model tuning?
31. What is a precision-recall curve, and how is it useful?
32. Explain the concept of explainable AI (XAI).
33. How does the architecture of a neural network affect its performance?
34. What are the ethical considerations in developing AI systems?
35. How does transfer learning benefit applications with limited data?
36. What is the significance of the ROC curve in model evaluation?
37. How does data augmentation enhance training datasets?
38. What is the role of batch normalization in deep learning?
39. How do neural networks handle missing data?
40. What are the implications of bias in AI algorithms?