Practice Variants Of Gradient Descent (6.4.2)

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6 sample questions

Question 1.
What is the primary advantage of Batch Gradient Descent?
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Think about the size of data used for computation.
Question 2.
How does SGD differ from Batch Gradient Descent?
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Consider the amount of data processed at once.
3Question 3.
Which gradient descent variant uses the entire dataset to compute the gradient?
- Batch Gradient Descent
- Stochastic Gradient Descent
- Mini-batch Gradient Descent
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Think about data usage during the computation.
4Question 4.
True or False: Mini-batch Gradient Descent is slower than both Batch and Stochastic Gradient Descent.
- True
- False
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Consider the definitions of these methods.
Question 5.
Using a dataset simulation, analyze why Mini-batch Gradient Descent might yield a more consistent performance over Batch and SGD.
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Consider examining iterations over several epochs.
Question 6.
Propose modifications to Stochastic Gradient Descent to reduce its noise during convergence.
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Think about adjustments in update calculations.

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