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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does 'data hungry' mean in the context of neural networks?
💡 Hint: Think about the food analogy.
Question 2
Easy
Name one consequence of overfitting in a neural network.
💡 Hint: Think about how well it does on training versus new data.
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 'data hungry' refer to?
- A need for large amounts of unlabeled data
- Need for large amounts of labeled data
- A model that does not learn
💡 Hint: Think about data requirements.
Question 2
True or False: Neural networks can easily explain how they make their decisions.
- True
- False
💡 Hint: Think about interpretability.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Develop a strategy to improve a neural network model that is underperforming due to overfitting. Include specific techniques you would use and why.
💡 Hint: Consider ways to modify training practices and structures.
Question 2
Discuss how the black box nature of neural networks can pose ethical challenges in applications like credit scoring. What approaches could be taken to alleviate these concerns?
💡 Hint: Think about responsibility and fairness in AI applications.
Challenge and get performance evaluation