Practice Feature Maps (activation Maps): The Output Of Convolution (6.2.2.2) - Introduction to Deep Learning (Weeks 12)
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Feature Maps (Activation Maps): The Output of Convolution

Practice - Feature Maps (Activation Maps): The Output of Convolution

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is a feature map?

💡 Hint: Think about what happens to the image during the filter application.

Question 2 Easy

Name one parameter that affects the convolution operation.

💡 Hint: These parameters dictate how the filter interacts with the input.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the role of a feature map in CNNs?

To flatten images
To store filter weights
To represent detected features

💡 Hint: Think about what feature maps are designed to do.

Question 2

True or False: Padding always decreases the size of the output feature map.

True
False

💡 Hint: Consider the purpose of adding zeros around the input.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Create a diagram illustrating the convolution operation and the resulting feature map for a simple 5x5 image and a 3x3 filter.

💡 Hint: Start with understanding what happens at each step of the convolution.

Challenge 2 Hard

Analyze how changing the stride from 1 to 3 would affect the output feature map size and interpret what that means for feature detection.

💡 Hint: Think about how each stride value translates into filter positions on the input.

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Reference links

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