Practice Batch Normalization (6.3.2) - Introduction to Deep Learning (Weeks 12)
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Batch Normalization

Practice - Batch Normalization

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary purpose of Batch Normalization?

💡 Hint: Think about why standardizing inputs might help during training.

Question 2 Easy

Which two parameters are learned in the Batch Normalization process?

💡 Hint: They're often linked to the adjustments made post-normalization.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main benefit of using Batch Normalization in training deep learning models?

It reduces overfitting
It normalizes activations
It increases model complexity

💡 Hint: Think about the core function of normalization.

Question 2

Batch Normalization can help increase the learning rate during training. True or False?

True
False

💡 Hint: Consider how normalization interacts with rate settings.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Analyze a neural network's performance with and without Batch Normalization across multiple epochs. Discuss the differences in training speed and accuracy.

💡 Hint: Focus on performance indicators for effective training.

Challenge 2 Hard

Design an experiment where you compare two CNNs—one with Batch Normalization and another without. Outline potential outcomes and hypothesize their impact on overfitting.

💡 Hint: Consider metrics for success like validation accuracy and loss.

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Reference links

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