Practice Variants of Gradient Descent - 6.4.2 | 6. Optimization Techniques | Numerical Techniques
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the primary advantage of Batch Gradient Descent?

πŸ’‘ Hint: Think about the size of data used for computation.

Question 2

Easy

How does SGD differ from Batch Gradient Descent?

πŸ’‘ Hint: Consider the amount of data processed at once.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

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?

  • Batch Gradient Descent
  • Stochastic Gradient Descent
  • Mini-batch Gradient Descent

πŸ’‘ 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.

  • True
  • False

πŸ’‘ Hint: Consider the definitions of these methods.

Solve 1 more question and get performance evaluation

Challenge Problems

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.

πŸ’‘ Hint: Consider examining iterations over several epochs.

Question 2

Propose modifications to Stochastic Gradient Descent to reduce its noise during convergence.

πŸ’‘ Hint: Think about adjustments in update calculations.

Challenge and get performance evaluation