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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?
💡 Hint: Think about the measure of error in predictions.
Question 2
Easy
Explain one advantage of Batch Gradient Descent.
💡 Hint: Consider how using all data might impact accuracy.
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 Batch Gradient Descent primarily used for?
- To minimize cost functions using subsets of data
- To compute updates using the whole dataset
- To find maximum error
- To increase computational cost
💡 Hint: Look for the definition of Batch Gradient Descent.
Question 2
True or False: Batch Gradient Descent guarantees convergence for non-convex functions.
- True
- False
💡 Hint: Consider the shape of the cost function.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a scenario where Batch Gradient Descent would outperform Stochastic Gradient Descent. Describe the characteristics of the dataset.
💡 Hint: Consider the size and nature of the dataset when determining efficiency.
Question 2
You are training a model with a significant number of features. Discuss how you can use Batch Gradient Descent effectively despite potential maximum likelihood issues.
💡 Hint: Think about handling multicollinearity and the curse of dimensionality.
Challenge and get performance evaluation