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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the primary advantage of Batch Gradient Descent?
π‘ Hint: Think about the size of data used for computation.
Question 2
Easy
How does SGD differ from Batch Gradient Descent?
π‘ Hint: Consider the amount of data processed at once.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
Which gradient descent variant uses the entire dataset to compute the gradient?
π‘ Hint: Think about data usage during the computation.
Question 2
True or False: Mini-batch Gradient Descent is slower than both Batch and Stochastic Gradient Descent.
π‘ Hint: Consider the definitions of these methods.
Solve 1 more question and get performance evaluation
Push your limits with challenges.
Question 1
Using a dataset simulation, analyze why Mini-batch Gradient Descent might yield a more consistent performance over Batch and SGD.
π‘ Hint: Consider examining iterations over several epochs.
Question 2
Propose modifications to Stochastic Gradient Descent to reduce its noise during convergence.
π‘ Hint: Think about adjustments in update calculations.
Challenge and get performance evaluation