Practice Gradient Descent (gd) (2.3.1) - Optimization Methods - Advance Machine Learning
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Gradient Descent (GD)

Practice - Gradient Descent (GD)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does gradient descent aim to minimize?

💡 Hint: Think about what we want to achieve in optimization.

Question 2 Easy

What does the learning rate control in gradient descent?

💡 Hint: Consider how quickly or slowly we want to adjust our model.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the purpose of the learning rate in gradient descent?

To control how fast parameters are updated
To determine the initial parameters
To regulate the objective function

💡 Hint: It is related to how quickly adjustments are made during optimization.

Question 2

True or False: Gradient descent can only find global minima.

True
False

💡 Hint: Consider the landscape of an optimization function.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Illustrate a situation where adjusting the learning rate significantly changes the convergence speed of gradient descent from local to global minimum.

💡 Hint: Experiment with different values and visualize their paths.

Challenge 2 Hard

Given a function with known local minima, describe a method to ensure gradient descent finds the global minimum, outlining the potential strategies such as momentum or random restarts.

💡 Hint: Consider utilizing history in parameter updates to smooth out paths.

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Reference links

Supplementary resources to enhance your learning experience.