| S.No | Question |
|---|---|
| 1. | Can you explain the concept of deep learning and its significance in machine learning? |
| 2. | What are the key differences between shallow learning and deep learning models? |
| 3. | Can you provide examples of popular deep learning models and their applications? |
| 4. | How does the architecture of deep learning models contribute to their performance? |
| 5. | What are some challenges or limitations of deep learning models? |
| 6. | Deep Learning Platforms and Software Libraries: |
| 7. | Name some popular deep learning platforms and software libraries used in the industry. |
| 8. | What are the key features or functionalities offered by deep learning platforms? |
| 9. | Can you compare and contrast different deep learning platforms in terms of their strengths and weaknesses? |
| 10. | How do these platforms support model training, deployment, and scalability? |
| 11. | Share an example of a project where you have used a deep learning platform or software library. |
| 12. | What is TensorFlow, and how does it relate to deep learning? |
| 13. | Explain the key components and architecture of TensorFlow. |
| 14. | How does TensorFlow facilitate the creation and training of deep learning models? |
| 15. | Can you discuss the advantages and disadvantages of using TensorFlow? |
| 16. | Share an example of a project where you have used TensorFlow. |
| 17. | What is a convolutional neural network (CNN), and what makes it suitable for image analysis? |
| 18. | Explain the main building blocks of a CNN architecture. |
| 19. | How do CNNs handle feature extraction and hierarchical learning? |
| 20. | Can you discuss some common applications of CNNs in computer vision? |
| 21. | Share an example of a project where you have implemented a CNN. |
| 22. | What is a recurrent neural network (RNN), and what are its distinguishing characteristics? |
| 23. | Explain the concept of sequential data processing using RNNs. |
| 24. | How do RNNs handle the challenges of processing variable-length sequences? |
| 25. | Can you provide examples of applications where RNNs are commonly used? |
| 26. | Share an example of a project where you have utilized RNNs. |
| 27. | How do you define natural language processing (NLP) and its role in machine learning? |
| 28. | What are the main challenges in processing natural language data? |
| 29. | Can you provide examples of NLP applications in real-world scenarios? |
| 30. | How does NLP contribute to tasks like sentiment analysis, text classification, or machine translation? |
| 31. | Discuss the ethical considerations associated with NLP. |
| 32. | Explain the concept of natural language understanding (NLU) and its importance in NLP. |
| 33. | How do techniques like tokenization, stemming, and lemmatization contribute to NLU? |
| 34. | What are some common methods for named entity recognition (NER) in NLP? |
| 35. | Discuss the role of word embeddings, such as Word2Vec or GloVe, in NLU. |
| 36. | Share an example of a project where you have applied NLU techniques. |
| 37. | Name some popular natural language processing libraries used in the industry. |
| 38. | What are the key features or functionalities offered by these libraries? |
| 39. | Can you compare and contrast different NLP libraries in terms of their strengths and weaknesses? |
| 40. | How do these libraries support tasks like text preprocessing, feature extraction, or model training? |
| 41. | Share an example of a project where you have used an NLP library. |
| 42. | How do machine learning and deep learning techniques contribute to NLP tasks? |
| 43. | Explain the concept of feature engineering in NLP and its importance. |
| 44. | What are some common machine learning algorithms used in NLP, such as Naive Bayes or Support Vector Machines? |
| 45. | How can deep learning models, such as recurrent neural networks or transformers, improve NLP performance? |
| 46. | Discuss the challenges of training deep learning models for NLP tasks. |
| 47. | Discuss the options for sharing reports and dashboards in Power BI (Power BI Pro/Premium). |
| 48. | What are some common techniques used for speech feature extraction? |
| 49. | Can you discuss the challenges of dealing with noise and variability in speech recognition? |
| 50. | What are the applications of speech recognition in real-world scenarios? |
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