Practice Basic Cnn Architectures: Stacking The Layers (6.2.4) - Introduction to Deep Learning (Weeks 12)
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Basic CNN Architectures: Stacking the Layers

Practice - Basic CNN Architectures: Stacking the Layers

Learning

Practice Questions

Test your understanding with targeted questions

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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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.

Get performance evaluation

Reference links

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