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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is model parallelism?
💡 Hint: Think about how a big task could be managed by breaking it into smaller parts.
Question 2
Easy
Give an example of model parallelism.
💡 Hint: Consider what large neural networks might need.
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 model parallelism?
- A method to scale CPU usage
- A technique of distributing model components
- Increasing the size of data
💡 Hint: It helps in working with large models.
Question 2
True or False: Model parallelism is only applicable to data parallel systems.
- True
- False
💡 Hint: Consider what each term refers to.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are tasked with designing a large-scale facial recognition system using a deep convolutional neural network (CNN). Discuss how you would implement model parallelism in this scenario.
💡 Hint: Think about layer responsibilities in CNNs.
Question 2
Critically evaluate the advantages and difficulties of using model parallelism over data parallelism in a real-time image processing application.
💡 Hint: Consider the type of workload each method handles.
Challenge and get performance evaluation