Practice Adagrad (2.4.3) - Optimization Methods - Advance Machine Learning
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Adagrad

Practice - Adagrad

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does Adagrad do?

💡 Hint: Think about what happens when parameters are updated frequently.

Question 2 Easy

Name one benefit of using Adagrad.

💡 Hint: Focus on the stability aspect.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main purpose of Adagrad?

To provide a static learning rate for all parameters
To adjust learning rates based on updates
To use only first derivatives

💡 Hint: Consider how regular and abnormal updates affect learning.

Question 2

True or False: Adagrad can lead to very small learning rates over time.

True
False

💡 Hint: Think about how learning rates react to update frequencies.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a learning rate of 0.1, and past gradients of [0.04, 0.01, 0.06], calculate the final learning rate adjustments.

💡 Hint: Remember to use the formula involving G in the denominator and apply it for each gradient.

Challenge 2 Hard

Describe a scenario in a machine learning project where using Adagrad might lead to better performance compared to static learning rate methods.

💡 Hint: Consider the implications of frequent updates versus rare feature abnormalities.

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Reference links

Supplementary resources to enhance your learning experience.