Practice - Mini-Batch Gradient Descent
Practice Questions
Test your understanding with targeted questions
What is Mini-Batch Gradient Descent?
💡 Hint: Think about how it differs from Batch and Stochastic methods.
Why is Mini-Batch Gradient Descent commonly used?
💡 Hint: Consider how it combines the features of both other methods.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does Mini-Batch Gradient Descent primarily use for parameter updates?
💡 Hint: Recall the differences from Stochastic and Batch methods.
True or False: Mini-Batch Gradient Descent provides updates that can be more stable than Stochastic Gradient Descent.
💡 Hint: Think about how averaging works within the context of training.
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Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.