Practice Batch Gradient Descent (3.2.1) - Supervised Learning - Regression & Regularization (Weeks 3)
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Batch Gradient Descent

Practice - Batch Gradient Descent

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does Gradient Descent aim to minimize?

💡 Hint: Think about the measure of error in predictions.

Question 2 Easy

Explain one advantage of Batch Gradient Descent.

💡 Hint: Consider how using all data might impact accuracy.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is Batch Gradient Descent primarily used for?

To minimize cost functions using subsets of data
To compute updates using the whole dataset
To find maximum error
To increase computational cost

💡 Hint: Look for the definition of Batch Gradient Descent.

Question 2

True or False: Batch Gradient Descent guarantees convergence for non-convex functions.

True
False

💡 Hint: Consider the shape of the cost function.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a scenario where Batch Gradient Descent would outperform Stochastic Gradient Descent. Describe the characteristics of the dataset.

💡 Hint: Consider the size and nature of the dataset when determining efficiency.

Challenge 2 Hard

You are training a model with a significant number of features. Discuss how you can use Batch Gradient Descent effectively despite potential maximum likelihood issues.

💡 Hint: Think about handling multicollinearity and the curse of dimensionality.

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Reference links

Supplementary resources to enhance your learning experience.