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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 want to achieve in optimization.
Question 2
Easy
What does the learning rate control in gradient descent?
π‘ Hint: Consider how quickly or slowly we want to adjust our model.
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 purpose of the learning rate in gradient descent?
- To control how fast parameters are updated
- To determine the initial parameters
- To regulate the objective function
π‘ Hint: It is related to how quickly adjustments are made during optimization.
Question 2
True or False: Gradient descent can only find global minima.
- True
- False
π‘ Hint: Consider the landscape of an optimization function.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Illustrate a situation where adjusting the learning rate significantly changes the convergence speed of gradient descent from local to global minimum.
π‘ Hint: Experiment with different values and visualize their paths.
Question 2
Given a function with known local minima, describe a method to ensure gradient descent finds the global minimum, outlining the potential strategies such as momentum or random restarts.
π‘ Hint: Consider utilizing history in parameter updates to smooth out paths.
Challenge and get performance evaluation