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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What are hyperparameters?
💡 Hint: Think about what you set before starting to train your model.
Question 2
Easy
Why do we use cross-validation?
💡 Hint: Consider how often you confirm results in other areas.
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 a hyperparameter?
- A value learned during training
- A pre-defined configuration for learning
- An output class
💡 Hint: Think about what you specify before the training starts.
Question 2
What is the advantage of Grid Search?
- Speed
- Exhaustiveness
- Randomness
💡 Hint: Consider the trade-off between thoroughness and speed.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Suppose you are tasked with tuning a complex neural network with ten hyperparameters, using both Grid Search and Random Search. Discuss the strategy you would use to balance computational resources and effectiveness.
💡 Hint: Consider the time trade-offs and risk of missing optimal values.
Question 2
You observe that despite using Grid Search, your model still performs poorly. Analyze possible reasons and suggest alternative strategies.
💡 Hint: Reflect on the criteria for setting hyperparameter ranges.
Challenge and get performance evaluation