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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of reshaping image data for CNNs?
💡 Hint: Think about how the data must be structured for proper processing.
Question 2
Easy
What normalization technique is applied to image pixel values?
💡 Hint: Consider the range of pixel values commonly found in images.
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 shape do CNNs require for input images?
- (batch_size
- height
- width)
- (height
- width
- channels)
- (batch_size
- height
- width
- channels)
💡 Hint: Consider how the data is structured with multiple images.
Question 2
True or False: Normalizing image data helps achieve faster convergence.
- True
- False
💡 Hint: Think about the effects of large value ranges on learning optimizations.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a color image dataset for cats and dogs, measuring 128x128 pixels, stored in an array of shape (2000, 128, 128, 3). You want to train a CNN. What additional steps will you take to prepare your data? Discuss the steps and justify their importance.
💡 Hint: Consider each stage in data preparation and how they collectively enhance model performance.
Question 2
Explain how poor normalization could lead to issues in training a CNN. What specific problems might arise?
💡 Hint: Think about the impact of inconsistent scales on learning algorithms.
Challenge and get performance evaluation