Practice Quantization - 3.1 | AI for Edge Devices and Internet of Things | Artificial Intelligence Advance
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3.1 - Quantization

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is quantization in the context of AI models?

💡 Hint: Think about changing the representation of numbers.

Question 2

Easy

What are the two primary methods of implementing quantization?

💡 Hint: Consider whether the model is pre-trained or not.

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 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

💡 Hint: Think about why low precision would be helpful for AI.

Question 2

True or False: Quantization-Aware Training adjusts the model after it has been trained.

  • True
  • False

💡 Hint: Consider when the adjustments take place.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Focus on how accuracy impacts the application in question.

Question 2

Evaluate a scenario where post-training quantization results in unexpected accuracy loss. What strategies could be utilized to address this issue?

💡 Hint: Consider both training adjustments and alternative quantization strategies.

Challenge and get performance evaluation