Practice - Building a Basic CNN Architecture using Keras
Practice Questions
Test your understanding with targeted questions
What is the purpose of normalizing image data in CNN?
💡 Hint: Think about how raw pixel values could affect learning.
Name one commonly used optimizer in CNN training.
💡 Hint: This is a popular choice due to its adaptive learning rate.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the purpose of a convolutional layer in a CNN?
💡 Hint: Consider what happens to the input images in this layer.
True or False: Pooling layers help to maintain all spatial information in the feature maps.
💡 Hint: Think about what pooling is designed to do.
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Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
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