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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the main purpose of using momentum in optimization?
💡 Hint: Think about the advantages of using past information.
Question 2
Easy
Define the term 'learning rate.'
💡 Hint: Consider how changes are applied in gradient descent.
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 formula for the momentum update?
- v_t = γv_{t-1} + η∇J(θ); θ := θ - v_t
💡 Hint: Look for the structure that involves both current and past information.
Question 2
True or False: Momentum helps in faster convergence during optimization.
- True
- False
💡 Hint: Think about how acceleration can affect motion.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
In a scenario with conflicting gradients, analyze how momentum would impact convergence, particularly in avoiding local minima while navigating restrictions.
💡 Hint: Consider how momentum can act like a strong current helping a boat move through varying waters.
Question 2
Evaluate how different values of γ (e.g., 0.1 vs 0.9) change the behavior of convergence in a noisy environment.
💡 Hint: Reflect on how responsiveness varies in fast vs slow reactions—sometimes a slower response is more stable.
Challenge and get performance evaluation