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Question 43 MSQ · 4.0 marks
Consider a neuron with binary inputs x1 and x2, and an output y. The neuron computes the
weighted sum of its inputs and produces an output according to a threshold. The threshold is
denoted as θ. The activation function is such that y = 1 if the weighted sum is greater than or equal
to θ, otherwise y = 0.
Which of the following statements are correct regarding the neuron’s ability to represent logical
AND and OR functions?
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The neuron can implement the AND function by setting appropriate weights
and a threshold.
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The neuron can implement the OR function by setting appropriate weights and
a threshold.
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There exists a single set of weights and a threshold that allows the same
neuron to correctly implement both the AND and OR functions simultaneously.
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Neurons are limited to implementing either the AND or the OR function and
cannot represent both simultaneously.
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Suppose we have a perceptron with two inputs, x1 and x2. This perceptron undergoes training on a
small dataset containing three points: (−1, 2) labeled as class 0, (0,−1) labeled as class 1, and (2, 1)
labeled as class 0. The weights of the perceptron are initialized to zeros, and the model is trained
until it reaches convergence. Given this scenario, what would be the assigned output class by the
trained perceptron for the new point (−2, 0)?
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You are training a neural network for sentiment analysis on a dataset of 10,000 text reviews. The
dataset is divided into 80% for training and 20% for testing. You decide to use Minibatch Gradient
Descent with a batch size of 32. If you perform a total of 100 epochs, how many parameter
updates will be performed in total?
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Let’s assume a continuous function f(x1,x2) is approximated using 3D tower function with a 3
hidden layer neural network using 100 towers. How many minimum number of neurons will we
need to approximate the function f(x1,x2) ?
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201
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300
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801
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701
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800
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Based on the above data, answer the given subquestions.
Suppose that all elements in the input vector are zero and the corresponding true label is also 0.
Further, suppose that all the parameters are initialized to zero.
What is the loss value if cross-entropy loss is used? Use natural logarithm ln.
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Based on the above data, answer the given subquestions.
Assuming that all weights between layers **h3** and **O** are initialized to one, with no bias associated
with any neuron, what would be the computed cross-entropy loss for a given single data point? If
the provided information is insufficient, please enter −1.
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