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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the primary purpose of an optimizer in neural networks?
💡 Hint: Think about how we learn from mistakes.
Question 2
Easy
Describe one disadvantage of using Stochastic Gradient Descent.
💡 Hint: Consider how updates occur.
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 does an optimizer do in a neural network?
- Adjusts model architecture
- Modifies weights and biases
- Preprocesses input data
💡 Hint: Focus on what helps reduce errors in predictions.
Question 2
True or False: Adam optimizer is known for requiring extensive hyperparameter tuning.
- True
- False
💡 Hint: Consider how adaptive it is.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Choose an optimizer for a high-dimensional image classification task and justify your choice. Discuss the advantages and disadvantages.
💡 Hint: Think about the data's complexity.
Question 2
Explain how you would approach training a neural network for a non-stationary objective. Which optimizer would you choose and why?
💡 Hint: Focus on how the optimizer reacts to changing data.
Challenge and get performance evaluation