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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is quantization?
💡 Hint: Think about how we simplify fractions.
Question 2
Easy
What does pruning do to a neural network?
💡 Hint: It's similar to trimming a tree.
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 technique reduces the precision of model weights?
- Pruning
- Quantization
- Knowledge Distillation
💡 Hint: Think about how numbers are represented.
Question 2
True or False: Pruning always increases the accuracy of the model.
- True
- False
💡 Hint: Consider the trade-offs.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a tinyML project for remote environmental sensing. Explain how you would optimize the model for edge deployment.
💡 Hint: Think about power efficiency.
Question 2
Analyze the trade-offs when using pruning vs quantization on a deep learning model.
💡 Hint: Consider how they both impact performance and deployment.
Challenge and get performance evaluation