Practice Quantization (3.1) - AI for Edge Devices and Internet of Things

Practice: Quantization

Lesson 3.1AI for Edge Devices and Internet of Things

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8 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is quantization in the context of AI models?
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Think about changing the representation of numbers.
Question 2.
What are the two primary methods of implementing quantization?
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Consider whether the model is pre-trained or not.
3Question 3.
What is the main purpose of quantization in AI models?
- To reduce model size
- To increase model size
- To delay model inference
- To ignore model accuracy
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Think about why low precision would be helpful for AI.
4Question 4.
True or False: Quantization-Aware Training adjusts the model after it has been trained.
- True
- False
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Consider when the adjustments take place.
Question 5.
Consider an edge device application requiring real-time decision making in a healthcare environment. Discuss how quantization could be tailored to maintain accuracy while minimizing response time.
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Focus on how accuracy impacts the application in question.
Question 6.
Evaluate a scenario where post-training quantization results in unexpected accuracy loss. What strategies could be utilized to address this issue?
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Consider both training adjustments and alternative quantization strategies.

4 more questions in this section. Enroll free to practise them all.