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An activation function is the function in an artificial neuron that delivers an output based on inputs. Activation functions in artificial neurons are an important part of the role that the artificial neurons play in modern artificial neural networks.
One way to understand the activation function is to look at a visual “model” of the artificial neuron. The activation function is at the “end” of the neural structure, and corresponds roughly to the axon of a biological neuron.
Another way to understand it is to look at the terminology around its use. IT professionals talk about the activation function when discussing either a binary output – either a 1 or a 0 – or a function that graphs a range of outputs based on inputs. In these cases, IT professionals and others often use the terms “transfer function” and “activation function” interchangeably, although the transfer function is more often associated with the graph that scans a range of outputs. Various functions guide the output that filters through the layers of the neural network to the final output layer of neurons or nodes.
It is also important to distinguish between linear and non-linear activation functions. Where linear activation functions maintain a constant, non-linear activation functions create more variation which utilizes the build of the neural network. Functions like sigmoid and ReLU are commonly used in neural networks to help build working models.