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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 do?
💡 Hint: Think about the goal of optimization tools.
Question 2
Easy
What is the learning rate?
💡 Hint: Consider what affects the speed of learning.
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 goal of Gradient Descent?
- To maximize a function
- To minimize a function
- To find a saddle point
💡 Hint: Think about the word 'optimize'.
Question 2
True or False: Stochastic Gradient Descent processes the full dataset for each update.
- True
- False
💡 Hint: Recall how Stochastic Gradient Descent operates.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Propose a scenario in which changing the learning rate dynamically during training could improve the convergence of Gradient Descent. Explain how you would implement this.
💡 Hint: Consider how small steps may help when you’re close to the minimum.
Question 2
Design a simple experiment to compare Batch Gradient Descent to Stochastic Gradient Descent on a given dataset, detailing the metrics you would use to measure performance.
💡 Hint: Think about how to measure indirect effects like computation time and accuracy.
Challenge and get performance evaluation