The Perceptron

Data Skeptic

Today's episode overviews the perceptron algorithm. This rather simple approach is characterized by a few particular features. It updates it's weights after seeing every example, rather than as a batch. It uses a step function as an activation function. It's only appropriate for linearly separable data, and it will converge to a solution if the data meets this criteria. Being a fairly simple algorithm, it can run very efficiently. Although we don't discuss it in this episode, multi-layer perceptron networks are what makes this technique most attract

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📆 2017-03-03 01:00