Practice Gradient-based Optimization (2.3) - Optimization Methods - Advance Machine Learning
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Gradient-Based Optimization

Practice - Gradient-Based Optimization

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does Gradient Descent aim to do?

💡 Hint: Think about the goal of optimization tools.

Question 2 Easy

What is the learning rate?

💡 Hint: Consider what affects the speed of learning.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main goal of Gradient Descent?

To maximize a function
To minimize a function
To find a saddle point

💡 Hint: Think about the word 'optimize'.

Question 2

True or False: Stochastic Gradient Descent processes the full dataset for each update.

True
False

💡 Hint: Recall how Stochastic Gradient Descent operates.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Propose a scenario in which changing the learning rate dynamically during training could improve the convergence of Gradient Descent. Explain how you would implement this.

💡 Hint: Consider how small steps may help when you’re close to the minimum.

Challenge 2 Hard

Design a simple experiment to compare Batch Gradient Descent to Stochastic Gradient Descent on a given dataset, detailing the metrics you would use to measure performance.

💡 Hint: Think about how to measure indirect effects like computation time and accuracy.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.