Where Perceptrons Fail

Distinguish between linearly separable data and non-linearly separable data.

Perceptrons are simple, and they can be assembled into larger structures like machine learning construction bricks. However, that simplicity comes with a distressing limitation that perceptrons work well on some datasets and fail badly on others. More specifically, perceptrons are a good fit for linearly separable data. Let’s see what linearly separable means, and why it matters.

Linearly separable data

Let’s look at this two-dimensional dataset:

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