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김태오
Perceptron 본문
Artificial Neuron: A perceptron is an artificial neuron or a simplified model of a biological neuron, used as a building block for artificial neural networks.
Binary Classification: Perceptrons are primarily used for binary classification tasks, where the goal is to categorize input data into one of two possible classes or categories.
Linear Model: It is a linear model that takes a weighted sum of input features, adds a bias term, and applies an activation function (typically a step function) to produce an output.
Learning Algorithm: Perceptrons utilize a simple learning algorithm, which iteratively adjusts weights and biases based on the prediction errors made on the training data, in order to minimize classification errors.
Limitations: Perceptrons can only solve linearly separable problems and are limited in their ability to learn complex, non-linear patterns in data, which is overcome by using multi-layer perceptrons or other advanced neural network architectures.
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