Practice Gradient Descent (3.2) - Supervised Learning - Regression & Regularization (Weeks 3)
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Gradient Descent

Practice - Gradient Descent

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is Gradient Descent?

💡 Hint: Think about finding the lowest point when you can't see the landscape.

Question 2 Easy

What does the learning rate (α) control?

💡 Hint: How quickly do you want to adjust your position?

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary purpose of Gradient Descent?

To maximize errors
To minimize errors
To stabilize learning

💡 Hint: Remember its role in optimization.

Question 2

True or False? Batch Gradient Descent uses a single data point for each update.

True
False

💡 Hint: Think about how 'batch' implies completeness.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design an algorithm to implement Batch Gradient Descent for a linear regression model. Discuss how you would handle different learning rates and termination conditions.

💡 Hint: What might you monitor to determine when to stop updating?

Challenge 2 Hard

Compare the convergence behavior of Stochastic Gradient Descent and Batch Gradient Descent on a noisy dataset. What adjustments might you suggest to improve performance?

💡 Hint: How can the size of batches employed help manage noise?

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Reference links

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