6.5 - Schedulers
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What is a learning rate?
💡 Hint: Think about how quickly the model makes adjustments to errors.
What does a Step Decay scheduler do?
💡 Hint: Consider the term 'step' in its name.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does a scheduler in deep learning mainly affect?
💡 Hint: Focus on what a scheduler is designed to do.
Is it true that learning rate warm-up starts with a high rate?
💡 Hint: Think about the term ‘warm-up’.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
Evaluate a situation where a model trained with no scheduler fails to converge. Propose a scheduling strategy that could rectify this issue.
💡 Hint: Think about dynamically adjusting the learning pace.
Create a hypothetical training scenario: describe initial learning rates and adjustments made by various schedulers. Analyze their impact on model performance.
💡 Hint: Consider how the model's learning evolves with each approach.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.