Practice Model Optimization For Edge Ai (3) - AI for Edge Devices and Internet of Things

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10 questions

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4 cards from this section. Quick, good the night before a test.
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6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is quantization?
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Think about how we simplify fractions.
Question 2.
What does pruning do to a neural network?
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It's similar to trimming a tree.
3Question 3.
What technique reduces the precision of model weights?
- Pruning
- Quantization
- Knowledge Distillation
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Think about how numbers are represented.
4Question 4.
True or False: Pruning always increases the accuracy of the model.
- True
- False
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Consider the trade-offs.
Question 5.
Design a tinyML project for remote environmental sensing. Explain how you would optimize the model for edge deployment.
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Think about power efficiency.
Question 6.
Analyze the trade-offs when using pruning vs quantization on a deep learning model.
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Consider how they both impact performance and deployment.

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