Machine Learning Quiz Questions

1. What is transfer learning in recommender systems?

view answer: A. The process of transferring knowledge between different types of recommender systems
Explanation: Transfer learning in recommender systems involves transferring knowledge between different types of recommender systems, such as collaborative filtering and content-based filtering.
2. What is one-shot learning?

view answer: A. Learning from a single example
Explanation: One-shot learning is the process of learning from a single example.
3. What is few-shot learning?

view answer: B. Learning from a small number of examples
Explanation: Few-shot learning is the process of learning from a small number of examples.
4. What is zero-shot learning?

view answer: D. Learning without any labeled examples from the target domain
Explanation: Zero-shot learning is the process of learning without any labeled examples from the target domain.
5. What is the primary motivation behind using transfer learning in machine learning?

view answer: B. To speed up the training process by leveraging pre-trained models
Explanation: The primary motivation behind using transfer learning is to speed up the training process by leveraging pre-trained models, allowing the model to benefit from knowledge learned in previous tasks and reducing the amount of training data and time required for the new task.
6. What are some popular pre-trained models used in transfer learning for natural language processing?

view answer: A. LSTM, GRU, Transformer
Explanation: LSTM, GRU, and Transformer are popular pre-trained models used in transfer learning for natural language processing.
7. What is domain adaptation in transfer learning?

view answer: A. The process of adapting a model trained on one domain to a new domain
Explanation: Domain adaptation is the process of adapting a model trained on one domain to a new domain.
8. What are the challenges of transfer learning?

view answer: A. The lack of transferability between tasks
Explanation: One of the challenges of transfer learning is the lack of transferability between tasks, which can limit the effectiveness of the technique.
9. What is feature extraction in transfer learning?

view answer: D. Using the pre-trained model for feature extraction and training a new model on top of the extracted features
Explanation: Feature extraction is the process of using the pre-trained model for feature extraction and training a new model on top of the extracted features.
10. What are some popular pre-trained models used in transfer learning for image recognition?

view answer: A. VGG, Inception, ResNet
Explanation: VGG, Inception, and ResNet are popular pre-trained models used in transfer learning for image recognition.
11. In which fields has transfer learning been successfully applied?

view answer: A. Computer vision and natural language processing
Explanation: Transfer learning has been successfully applied in computer vision and natural language processing, as well as other fields such as speech recognition and recommender systems.
12. What is fine-tuning in transfer learning?

view answer: C. Adding new layers to the pre-trained model and training the entire model on a new task
Explanation: Fine-tuning is the process of adding new layers to the pre-trained model and training the entire model on a new task.
13. What is transfer learning?

view answer: B. A technique for transferring data between different machine learning models
Explanation: Transfer learning is a mac_hine learning technique where a model trained on one task is reused as a starting point for a model on a second task.
14. What is the goal of transfer learning?

view answer: D. All of the above
Explanation: The goal of transfer learning is to improve the performance of a model on a new task by leveraging knowledge gained from a related task.
15. What are the two main types of transfer learning?

view answer: A. Unsupervised and supervised
Explanation: The two main types of transfer learning are unsupervised and supervised. In unsupervised transfer learning, the source and target tasks have different output variables, while in supervised transfer learning, the source and target tasks have the same output variables.

© aionlinecourse.com All rights reserved.