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5 cards from this section. Quick, good the night before a test.
What is represented by the inputs in a perceptron?
Think about what data characteristics might influence the prediction.
What does a weight determine in a perceptron?
Consider how different inputs may have different levels of significance.
What is the purpose of weights in a perceptron?
Consider how weights affect the computation.
True or False: The output of the sigmoid function is always greater than or equal to 0.
Think about the range of outputs for the sigmoid function.
Given inputs [2, 4, 6], weights [0.5, 0.3, 0.2], and a bias of 2, compute the output of the perceptron's summation function. Then choose an activation function (either Sigmoid or ReLU) and explain how it transforms the output.
Remember to apply the equation step by step!
Create a perceptron model for predicting whether a fruit is an apple or not based depending on the attributes: weight (grams), sweetness (0-10), and color score (1-10). Explain how weights might be adjusted through learning.
Consider how features might correlate to the classification.
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