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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does Adam stand for in deep learning?
💡 Hint: Think about how it relates to momentum in the learning process.
Question 2
Easy
What two types of moving averages does Adam maintain?
💡 Hint: One relates to momentum and the other to smoothing updates.
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 key feature sets Adam apart from basic Stochastic Gradient Descent?
- A) Fixed learning rate
- B) Adaptive learning rates
- C) Requires more parameters
💡 Hint: Consider the impact of adapting to parameters.
Question 2
True or False: Adam can sometimes converge to sub-optimal generalizations.
- True
- False
💡 Hint: Think about the common pitfalls in optimization.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Design a scenario where Adam would outperform SGD significantly. Discuss and justify your reasoning with examples.
💡 Hint: In which environments do we see the quickest changes in optimization?
Question 2
Compare and contrast Adam with another optimizer of your choice (e.g., RMSprop) with concrete examples based on their strengths and weaknesses.
💡 Hint: Focus on aspects such as gradient behavior and convergence.
Challenge and get performance evaluation