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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of a pooling layer in a CNN?
💡 Hint: What happens to feature dimensions after pooling?
Question 2
Easy
What does the input layer of a CNN do?
💡 Hint: Think about what enters the network first.
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 the convolutional layer in a CNN?
- To perform dimensionality reduction.
- To extract features from images.
- To normalize input data.
💡 Hint: Think about the layers that specifically detect patterns.
Question 2
True or False: The pooling layer increases the size of the feature map.
- True
- False
💡 Hint: Consider the effects of pooling on dimensions.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a CNN architecture suitable for classifying medical images. Explain the rationale behind your layer choices.
💡 Hint: Consider the importance of feature extraction in medical imaging.
Question 2
Critically analyze the impact of adding more convolutional and pooling layers in terms of computational costs versus performance gains. Discuss how this might affect model training.
💡 Hint: Think about efficiency and the risk of overfitting with more parameters.
Challenge and get performance evaluation