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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is Nesterov Accelerated Gradient?
💡 Hint: Think about how it differs from standard gradient approaches.
Question 2
Easy
What does momentum do in optimization?
💡 Hint: Consider the role of past velocities in calculations.
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 the key advantage of Nesterov Accelerated Gradient?
- It reduces the number of calculations
- It provides foresight in updates
- It only works with specific models
💡 Hint: Think about what 'looking ahead' means in an optimization context.
Question 2
True or False: The learning rate in NAG determines how quickly the algorithm can approach the minimum.
- True
- False
💡 Hint: Recall the role of learning rates in optimization.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a machine learning model suffering from slow convergence when using standard gradient descent. How could you implement NAG to address this issue within a deep learning framework?
💡 Hint: Think about integrating adjustments into a backpropagation training process.
Question 2
When training a neural network with NAG, you notice it occasionally overshoots the minimum. Propose a way to mitigate this effect.
💡 Hint: Reflect on how careful tuning can influence model training.
Challenge and get performance evaluation