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Knowledge
Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is transfer learning?
💡 Hint: Think about how we can leverage existing knowledge.
Question 2
Easy
What is meant by 'feature extraction'?
💡 Hint: Consider what happens when we freeze a model's layers.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the main advantage of transfer learning over training from scratch?
- Increased Data Requirements
- Reduced Training Time
- Increased Complexity
💡 Hint: Consider the efficiency gained by not starting from zero.
Question 2
True or False: Fine-tuning involves freezing all layers of a pre-trained model.
- True
- False
💡 Hint: Reflect on what fine-tuning entails in flexibility.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a new image classification task, detail a step-by-step plan to apply transfer learning effectively to achieve optimal results.
💡 Hint: Think through each component and how they serve your specific problem.
Question 2
You have a small dataset for a specific type of object recognition while the pre-trained model was trained on general objects. Discuss the advantages and disadvantages of using feature extraction versus fine-tuning for your scenario.
💡 Hint: Evaluate the trade-offs between efficiency and model performance.
Challenge and get performance evaluation