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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of normalizing image data in CNN?
💡 Hint: Think about how raw pixel values could affect learning.
Question 2
Easy
Name one commonly used optimizer in CNN training.
💡 Hint: This is a popular choice due to its adaptive learning rate.
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 purpose of a convolutional layer in a CNN?
- a. To classify images
- b. To reduce dimensions
- c. To extract features using filters
💡 Hint: Consider what happens to the input images in this layer.
Question 2
True or False: Pooling layers help to maintain all spatial information in the feature maps.
- True
- False
💡 Hint: Think about what pooling is designed to do.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design an architecture for a CNN that could classify images of 64x64 RGB images into 10 categories. Specify the number of layers, types of layers, and their configurations.
💡 Hint: Consider how feature extraction and dimensional reduction can be balanced.
Question 2
After training your model, you notice the training loss decreases while validation loss increases. Suggest at least two strategies to handle this situation.
💡 Hint: Think about what adjustments you can make to the model or the data.
Challenge and get performance evaluation