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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is overfitting in the context of deep learning?
💡 Hint: Think about the model's performance on new versus known data.
Question 2
Easy
What percentage of neurons might be dropped with Dropout?
💡 Hint: Consider the common format for representing dropout rates.
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 Dropout in deep learning?
- A) To improve the speed of training
- B) To prevent overfitting
- C) To reduce the number of epochs
- D) To increase the network depth
💡 Hint: Think about what Dropout achieves during the learning phase.
Question 2
True or False: Batch Normalization only normalizes outputs during the prediction phase.
- True
- False
💡 Hint: Focus on when normalizations take place.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Imagine you have a deep learning model that still overfits despite the use of regularization techniques. What steps would you take to address this issue?
💡 Hint: Think about techniques to enhance data diversity or limit model behavior.
Question 2
Provide a detailed comparison of when to use Dropout vs. Batch Normalization during model training.
💡 Hint: Consider the impact each technique has on model performance.
Challenge and get performance evaluation