Practice - Adam (Adaptive Moment Estimation)
Practice Questions
Test your understanding with targeted questions
What does Adam stand for in deep learning?
💡 Hint: Think about how it relates to momentum in the learning process.
What two types of moving averages does Adam maintain?
💡 Hint: One relates to momentum and the other to smoothing updates.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What key feature sets Adam apart from basic Stochastic Gradient Descent?
💡 Hint: Consider the impact of adapting to parameters.
True or False: Adam can sometimes converge to sub-optimal generalizations.
💡 Hint: Think about the common pitfalls in optimization.
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Challenge Problems
Push your limits with advanced challenges
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?
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.
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Reference links
Supplementary resources to enhance your learning experience.