Perceptron

Test your knowledge of perceptrons, their structure, activation functions, and their role in neural networks.

FREE QUIZ

9 Questions

~18 Minutes

Intermediate

Question 1

What is a perceptron in machine learning?

A type of data scaling method A simplified model of a neuron that makes decisions using weighted inputs and an activation function A loss function used in classification An optimization algorithm

Question 2

Which of the following is a key component of a perceptron?

Distance metric Confusion matrix Weights and bias Clustering coefficient

Question 3

What does the activation function in a perceptron do?

Calculates the loss value Applies a transformation to the weighted sum to produce the output Measures distance between vectors Sorts input values

Question 4

What distinguishes the sigmoid function from the step function?

It is not used in perceptrons It outputs only binary values It provides a continuous output between 0 and 1 and is differentiable It is faster to compute

Question 5

Why is the sigmoid function useful in training neural networks?

It always outputs 1 It simplifies data preprocessing It allows smooth gradients for backpropagation It does not require weights

Question 6

What does the bias term in a perceptron help with?

Normalizing the input data Scaling the inputs Shifting the activation function output Increasing learning rate

Question 7

In logic gate terms, which gate outputs 1 when at least one input is 1?

AND gate NOT gate OR gate XOR gate

Question 8

How does using a sigmoid activation function in a perceptron affect the output compared to a step function?

The output becomes a continuous value between 0 and 1 instead of just 0 or 1 The output is always equal to the input The output is a fixed value regardless of input The output becomes a negative number

Question 9

What does a perceptron adjust to minimize the difference between predicted and actual output?

Activation functions Gradient functions Weights Loss functions

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