Practice Conceptual Exploration of Hyperparameters - 6.5.2.6 | Module 6: Introduction to Deep Learning (Weeks 12) | Machine Learning
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6.5.2.6 - Conceptual Exploration of Hyperparameters

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is a hyperparameter?

πŸ’‘ Hint: Think about configuration settings of a model.

Question 2

Easy

What role do filters play in CNNs?

πŸ’‘ Hint: Consider what happens when we apply convolution to data.

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 is a primary role of hyperparameters in CNNs?

  • They are learned during training.
  • They define model architecture and training behavior.
  • They adjust weights after each epoch.

πŸ’‘ Hint: Think about how hyperparameters differ from regular model parameters.

Question 2

True or False: A higher dropout rate will always improve model performance.

  • True
  • False

πŸ’‘ Hint: Consider the trade-off in regularization techniques.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a CNN architecture for image classification. Choose appropriate hyperparameters for filters, dropout rates, and pooling sizes. Justify your choices.

πŸ’‘ Hint: Consider the size of your data and the risk of overfitting in your specifications.

Question 2

Critically evaluate the effects of underfitting and overfitting in your model. What hyperparameters could you adjust to address these issues easily?

πŸ’‘ Hint: Think about balancing performance and complexity in your model strategy.

Challenge and get performance evaluation