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I've always been a bit confused when it comes to Deep Learning terminology.

Is the definition of the perceptron, whether single layer or multi layer, associated with a specific type of activation function? (aka Step function?)

Or is it associated with any activation function that attempts to squash the output in between a range of values? (aka sigmoid, TanH..)

The definition of a perceptron is still not very clear to me, despite all the sources i've read.

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The term multilayer perceptron is used as a synonym for a normal feedforward neural network. It doesn't necessarily imply a specific kind of activation function.

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  • $\begingroup$ Well according to the wiki, the perceptron is an algorithm, under Supervised Learning, used to handle binary classifier-based problems. So if that is the case, then the activation function would have to be geared towards binary classifiers in general hinting at specific activation functions $\endgroup$ – Kamal Raydan Oct 20 '20 at 9:37
  • $\begingroup$ Yes, that's a sensible thing to assume. However that's not how the term is used today, which makes it rather misleading. I think it's better to avoid using the term. $\endgroup$ – Tom Dörr Oct 20 '20 at 11:59

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