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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does Gradient Descent aim to minimize?
π‘ Hint: Think about what we are trying to achieve in model training.
Question 2
Easy
Define the learning rate in the context of Gradient Descent.
π‘ Hint: It controls the speed of convergence.
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 role of the learning rate in Gradient Descent?
- Determines the speed of convergence
- Sets the initial parameter values
- Controls the cost function
π‘ Hint: Think about how quickly you go downhill.
Question 2
True or False: Batch Gradient Descent guarantees convergence faster than Stochastic Gradient Descent.
- True
- False
π‘ Hint: Consider which method accesses data differently.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a scenario where you notice that your model is converging very slowly when using Batch Gradient Descent. Discuss potential reasons and solutions.
π‘ Hint: Analyze the balance between speed and stability.
Question 2
Imagine you are working with a dataset featuring significant outliers. How might Stochastic Gradient Descent behave differently with this data compared to Batch Gradient Descent?
π‘ Hint: Think about the impact of observing the full dataset vs. isolated samples.
Challenge and get performance evaluation