Question 1

Consider a neuron with binary inputs *x*1 and *x*2, 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? Suppose we have a perceptron with two inputs, *x*1 and *x*2. 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)? 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?