Deep Learning End Term: 28 April 2024, Set QDB1 (January 2024 term)

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:

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.