Practice Normalization (6.5.2.1.3) - Introduction to Deep Learning (Weeks 12)
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Normalization

Practice - Normalization

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is normalization in the context of deep learning?

💡 Hint: Think about changes in training data.

Question 2 Easy

What does Batch Normalization aim to address?

💡 Hint: Consider the effect of changing input distributions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What primary problem does Batch Normalization aim to solve?

Overfitting
Internal covariate shift
Training time

💡 Hint: Consider the effect of changing input distributions.

Question 2

True or False: Normalization can lead to faster training of deep learning models.

True
False

💡 Hint: Think about the relationship between stability and training speed.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Evaluate why Batch Normalization might not be beneficial in small datasets. What factors could limit its effectiveness?

💡 Hint: Think about how sample size influences statistical accuracy.

Challenge 2 Hard

Design a mini-experiment where you compare performance with and without Batch Normalization on a CNN trained on CIFAR-10. Outline expected results and metrics.

💡 Hint: Consider the aspects of model comparison.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.