Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is overfitting in machine learning?
💡 Hint: Think about the model's performance on unseen data.
Question 2
Easy
What are the two types of regularization mentioned?
💡 Hint: Recall the names given for each type during the lesson.
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 primary purpose of regularization in machine learning?
- To enhance underfitting
- To reduce overfitting
- To avoid bias
💡 Hint: Think about what happens when a model loses its ability to generalize.
Question 2
True or False: L1 regularization can result in some weights being exactly zero.
- True
- False
💡 Hint: Recall the penalty applied in L1 regularization.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a neural network that exhibits significant overfitting during training. Propose a strategy combining L1 and L2 regularization to improve its performance.
💡 Hint: How can combining penalties help create a stronger model?
Question 2
Explain how you would implement dropout in a training regime for a deep learning model with several layers to balance robustness and performance.
💡 Hint: How do you ensure the model can still learn effectively while using dropout?
Challenge and get performance evaluation