6.4.2 - Variants of Gradient Descent
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Practice Questions
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What is the primary advantage of Batch Gradient Descent?
💡 Hint: Think about the size of data used for computation.
How does SGD differ from Batch Gradient Descent?
💡 Hint: Consider the amount of data processed at once.
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Interactive Quizzes
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Which gradient descent variant uses the entire dataset to compute the gradient?
💡 Hint: Think about data usage during the computation.
True or False: Mini-batch Gradient Descent is slower than both Batch and Stochastic Gradient Descent.
💡 Hint: Consider the definitions of these methods.
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Challenge Problems
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Using a dataset simulation, analyze why Mini-batch Gradient Descent might yield a more consistent performance over Batch and SGD.
💡 Hint: Consider examining iterations over several epochs.
Propose modifications to Stochastic Gradient Descent to reduce its noise during convergence.
💡 Hint: Think about adjustments in update calculations.
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