Practice - Stochastic Gradient Descent (SGD)
Practice Questions
Test your understanding with targeted questions
What is Stochastic Gradient Descent (SGD)?
💡 Hint: Think about how it differs from batch gradient descent.
Why is a learning rate important in SGD?
💡 Hint: Consider what happens if the learning rate is too high or too low.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does SGD stand for?
💡 Hint: Think about how it utilizes data differently from batch methods.
True or False: SGD calculates the gradient based on the entire dataset.
💡 Hint: Reflect on the definition of stochastic as it relates to the term 'entire dataset'.
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Challenge Problems
Push your limits with advanced challenges
Analyze SGD's performance on a dataset with varying levels of noise. How would you expect SGD’s oscillations to vary in this scenario?
💡 Hint: Consider how noise might influence the gradient calculations.
Given a dataset of 100,000 samples, design a mini-batch size for SGD that balances training time and convergence stability.
💡 Hint: Think about how batch sizes affect the update frequency.
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Reference links
Supplementary resources to enhance your learning experience.