Practice Regularization Techniques (7.7.2) - Deep Learning & Neural Networks
Students

Academic Programs

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

Engineering Courses
Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Certifications
Games

Interactive Games

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

Regularization Techniques

Practice - Regularization Techniques

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does L1 regularization encourage in a model?

💡 Hint: Think about how L1 regularization affects weight values.

Question 2 Easy

What is the role of dropout in training?

💡 Hint: Consider what happens to the neurons during each training pass.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the purpose of L2 regularization?

To encourage sparsity in weights
To penalize large weights
To add noise to the training data

💡 Hint: What happens to weights under L2?

Question 2

True or False: Early stopping continues training until all epochs are completed.

True
False

💡 Hint: What is the condition for early stopping?

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Consider a neural network trained on a small dataset with many features. Describe how applying L1 and L2 regularization could improve the model's generalization performance.

💡 Hint: Think about how the model behaves with fewer effective features.

Challenge 2 Hard

You observed that your model performs better on training data than validation data even after applying dropout. What steps can you take next considering batch normalization and early stopping?

💡 Hint: Consider how early detection of performance can help maintain a balance.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.