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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is Mini-Batch Gradient Descent?
💡 Hint: Think about how it differs from Batch and Stochastic methods.
Question 2
Easy
Why is Mini-Batch Gradient Descent commonly used?
💡 Hint: Consider how it combines the features of both other methods.
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
What does Mini-Batch Gradient Descent primarily use for parameter updates?
- Entire dataset
- Single data point
- Small subset of data
💡 Hint: Recall the differences from Stochastic and Batch methods.
Question 2
True or False: Mini-Batch Gradient Descent provides updates that can be more stable than Stochastic Gradient Descent.
- True
- False
💡 Hint: Think about how averaging works within the context of training.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Discuss the impact of mini-batch size on the convergence velocity and stability of the training process in a neural network.
💡 Hint: Consider how variance changes with batch size.
Question 2
Compare and contrast the computational requirements of Mini-Batch Gradient Descent with Batch Gradient Descent for a dataset with millions of entries.
💡 Hint: Think about processing time in relation to the dataset scale.
Challenge and get performance evaluation