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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of dropout in neural networks?
💡 Hint: Think about how it affects neuron collaboration.
Question 2
Easy
Define L1 regularization.
💡 Hint: Consider the effect on model complexity.
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 does dropout do in a neural network?
- A) Increases the number of neurons
- B) Randomly disables neurons during training
- C) Reduces the training set size
💡 Hint: Consider its role in preventing overfitting.
Question 2
True or False: L2 regularization always sets weights to zero.
- True
- False
💡 Hint: Think about the nature of the L2 penalty.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Devise a plan to experimentally test the effectiveness of dropout vs. L2 regularization on the same dataset.
💡 Hint: Consider the models' architecture and validation metrics you will employ.
Question 2
Evaluate the impact of setting a high penalty in L1 regularization on specific types of datasets.
💡 Hint: Reflect on model interpretability versus complexity.
Challenge and get performance evaluation