Practice Momentum (2.4.1) - Optimization Methods - Advance Machine Learning
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Momentum

Practice - Momentum

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Practice Questions

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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.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

Challenge 2 Hard

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.

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