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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does L1 regularization encourage in a model?
💡 Hint: Think about how L1 regularization affects weight values.
Question 2
Easy
What is the role of dropout in training?
💡 Hint: Consider what happens to the neurons during each training pass.
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 purpose of L2 regularization?
- To encourage sparsity in weights
- To penalize large weights
- To add noise to the training data
💡 Hint: What happens to weights under L2?
Question 2
True or False: Early stopping continues training until all epochs are completed.
- True
- False
💡 Hint: What is the condition for early stopping?
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a neural network trained on a small dataset with many features. Describe how applying L1 and L2 regularization could improve the model's generalization performance.
💡 Hint: Think about how the model behaves with fewer effective features.
Question 2
You observed that your model performs better on training data than validation data even after applying dropout. What steps can you take next considering batch normalization and early stopping?
💡 Hint: Consider how early detection of performance can help maintain a balance.
Challenge and get performance evaluation