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4 cards from this section. Quick, good the night before a test.
What does a Perceptron do?
Think about the decision-making process of the model.
What is meant by 'binary output'?
Remember the nature of classification tasks.
What type of problems can the Perceptron solve?
Think about what linear separability implies.
True or False: The Perceptron can output more than two classes.
Recall the definition of binary output.
Given a simple dataset represented on a plane with points (1,1), (2,2), (3,3), and (1,2), (3,1), determine if a Perceptron can classify them linearly.
Visualize the points on a graph and look for a clear line dividing them.
What are the implications of using the Perceptron for a dataset that is not linearly separable? Provide an example.
Consider the arrangement of points in the XOR dataset.
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