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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the benefit of normalizing data?
💡 Hint: Consider how data is represented to the model.
Question 2
Easy
Name a common optimizer used when compiling CNNs.
💡 Hint: Think about widely adopted techniques.
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 are the key components to specify when compiling a model?
- Loss Function
- Metrics
- Number of Epochs
- Optimizer
- Loss Function
- Metrics
- Learning Rate
- Layers
- Activation Function
💡 Hint: Consider what controls the model's learning.
Question 2
True or False: The pooling layer increases the spatial dimensions of the output feature maps.
- True
- False
💡 Hint: Think about how pooling affects the size of feature maps.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset where images are of varying sizes, how would you adjust your preprocessing steps?
💡 Hint: Think about how CNNs expect input dimensions.
Question 2
Design a simple CNN architecture for a classification task involving ten categories.
💡 Hint: Recall the structure discussed in class.
Challenge and get performance evaluation