Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of backpropagation?
💡 Hint: Think about how gradients help in training deep learning models.
Question 2
Easy
Define learning rate.
💡 Hint: Consider what happens to the model if the learning rate is too high or too low.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What technique is used to update weights in a neural network?
- Backpropagation
- Forward Propagation
- Data Augmentation
💡 Hint: Think about the method used for learning from errors.
Question 2
Is the learning rate constant throughout the training process? (True/False)
- True
- False
💡 Hint: Consider how learning rates can change for better optimization.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Explain how you would implement a custom learning rate scheduler in a deep learning framework. Provide a sample code snippet.
💡 Hint: Consider how learning rates might change during training epochs.
Question 2
Discuss how regularization techniques might affect model performance in different scenarios. Give an example.
💡 Hint: Thinking about the trade-off between training accuracy and generalization.
Challenge and get performance evaluation