Deep Learning, End Term

January 2024 term, 28 Apr 2024, Set QDB1

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Question 1

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Consider a scenario where you have a dataset with overlapping classes (that is instances from different classes share similar or identical feature values), and you decide to train a perceptron model for classification.
Assertion (A): The perceptron model may struggle to classify instances accurately when classes overlap in the feature space.
Reason (R): The perceptron learning algorithm aims to find a linear decision boundary that separates the classes, and in the presence of overlapping classes, it may not be able to capture the underlying patterns effectively.
Select the correct option:

Select one option

• A.

  Both A and R are true, and R is the correct explanation of A.

• B.

  Both A and R are true, but R is not the correct explanation of A.

• C.

  A is true, but R is false.

• D.

  A is false, but R is true.

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Consider a scenario where you have a dataset with overlapping classes (that is instances from different classes share similar or identical feature values), and you decide to train a perceptron model for classification.\ **Assertion (A):** The perceptron model may struggle to classify instances accurately when classes overlap in the feature space.\ **Reason (R):** The perceptron learning algorithm aims to find a linear decision boundary that separates the classes, and in the presence of overlapping classes, it may not be able to capture the underlying patterns effectively.\ Select the correct option: Figure from the original question paper In the context of mini-batch gradient descent, if doubling the size of the mini-batch makes your model take twice as many epochs to reach convergence, how does this affect the total number of parameter updates compared to using the original mini-batch size? Assume everything else remains constant.