Perceptron Learning 01
Автор: Michael Mananghaya
Загружено: 2025-11-18
Просмотров: 11
Описание:
Perceptron Learning How it works
Receives inputs: The perceptron takes multiple input values, which can be numerical data points or features.
Applies weights: Each input is multiplied by a corresponding weight, which represents the importance of that input.
Calculates a weighted sum: All the weighted inputs are added together, often with a bias term added to adjust the threshold.
Uses an activation function: The sum is then passed through a step function (an activation function).
Produces a binary output: If the sum exceeds a certain threshold, the perceptron outputs a 1 (or "yes"); otherwise, it outputs a 0 (or "no").
Learns from mistakes: During training, the algorithm adjusts the weights if its prediction is incorrect. It continues to adjust the weights until it can accurately classify the data, a process called the perceptron
learning rule.
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