Virtual Labs

Perceptron

What type of decision boundary does a single-layer perceptron create?

a: A circular boundary Explanation

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b: A linear hyperplane Explanation

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c: A polynomial curve Explanation

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d: Multiple disconnected regions Explanation

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What does it mean for a dataset to be linearly separable?

a: The data can be separated by a straight line or hyperplane Explanation

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b: The data points are arranged in a line Explanation

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c: The dataset has only linear features Explanation

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d: All data points have the same label Explanation

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What is the XOR problem in the context of perceptrons?

a: A linearly separable problem that perceptrons can easily solve Explanation

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b: A non-linearly separable problem that single-layer perceptrons cannot solve Explanation

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c: A problem with too much training data Explanation

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d: A convergence issue due to wrong learning rate Explanation

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In the perceptron learning rule, when are weights updated?

a: Only when the prediction is correct Explanation

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b: After every epoch regardless of errors Explanation

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c: Only when the perceptron makes an incorrect prediction Explanation

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d: Before making any predictions Explanation

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What happens to the weights when the perceptron makes a correct prediction?

a: They are reset to zero Explanation

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b: They remain unchanged Explanation

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c: They increase by the learning rate Explanation

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d: They are randomly reinitialised Explanation

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What role does the learning rate play in perceptron training?

a: It determines how many epochs to train Explanation

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b: It controls the step size of weight updates Explanation

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c: It sets the initial weights Explanation

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d: It defines the activation function Explanation

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What does the bias term 'b' allow the perceptron to do?

a: Increase the number of input features Explanation

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b: Move the decision boundary away from the origin Explanation

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c: Make the decision boundary curved Explanation

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d: Classify multiple classes simultaneously Explanation

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Which logic gate CAN be solved by a single-layer perceptron?

a: XOR gate Explanation

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b: XNOR gate Explanation

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c: AND gate Explanation

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d: None of the above Explanation

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What mathematical equation defines the decision boundary of a perceptron in 2D?

a: w1·x1 + w2·x2 + b = 0 Explanation

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b: w1·x1² + w2·x2² + b = 0 Explanation

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c: x1 + x2 = 1 Explanation

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d: sigmoid(w1·x1 + w2·x2 + b) = 0.5 Explanation

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What fundamental limitation did the XOR problem reveal about single-layer perceptrons?

a: They require too much training data Explanation

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b: They can only create linear decision boundaries Explanation

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c: They cannot handle binary inputs Explanation

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d: They are too slow to train Explanation

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