Practice Stochastic Gradient Descent (sgd) (3.2.2) - Supervised Learning - Regression & Regularization (Weeks 3)
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Stochastic Gradient Descent (SGD)

Practice - Stochastic Gradient Descent (SGD)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is Stochastic Gradient Descent?

💡 Hint: Consider how it contrasts with Batch Gradient Descent.

Question 2 Easy

Name one advantage of using SGD.

💡 Hint: Think about how data size affects computation.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does SGD stand for?

Stochastic Gradient Descent
Static Gradient Descent
Stochastic Gradient Definition

💡 Hint: Think about the first word - what does 'stochastic' mean?

Question 2

True or False: SGD always finds the global minimum.

True
False

💡 Hint: Consider the implications of noise in updates.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

In a scenario where an organization uses SGD to train a neural network, discuss how they might tune parameters to improve performance despite the noise in updates.

💡 Hint: Think about common neural network optimizations.

Challenge 2 Hard

Explore a real-world application of SGD beyond deep learning and analyze its effectiveness based on data type.

💡 Hint: Consider dynamic environments where data is constantly in flux.

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Reference links

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