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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is normalization?
💡 Hint: Think about what happens to data scales when using machine learning.
Question 2
Easy
Name one technique for data augmentation.
💡 Hint: Consider how you might visually change an image.
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 goal of normalization?
- To increase dataset size
- To speed up convergence during training
- To simplify data
- None of the above
💡 Hint: Think about why algorithms might struggle with inconsistent feature scales.
Question 2
True or False: Data augmentation can lead to overfitting.
- True
- False
💡 Hint: Consider the purpose of data augmentation in improving model performance.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
A dataset has features ranging from 0 to 100 and 1000 to 10,000. Explain how you would normalize these features for better training results.
💡 Hint: Focus on how feature scaling can affect learning.
Question 2
Design an experiment where data augmentation can improve image classification accuracy. Outline the methods you'll use and justify your choices.
💡 Hint: Reflect on the balance between diversity and relevance in training data.
Challenge and get performance evaluation