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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is momentum in optimization?
💡 Hint: Think of momentum as keeping you moving in the direction of your last update.
Question 2
Easy
What does Adagrad do?
💡 Hint: Consider how frequently a parameter has been modified.
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 does momentum do in optimization?
- Increases learning rate
- Adds previous updates to current updates
- Decreases gradient size
💡 Hint: Remember how momentum increases forward movement.
Question 2
True or False: Nesterov Accelerated Gradient does not consider previous updates.
- True
- False
💡 Hint: Think about predictions versus reactions.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a model that struggles to converge using standard gradient descent. How would you choose an optimizer from those discussed and why?
💡 Hint: Consider the advantages of adapting learning rates and smoothing updates.
Question 2
If given a dataset with highly variable features, which optimizer would you prefer and why?
💡 Hint: Think about which methods thrive in noisy landscapes.
Challenge and get performance evaluation