Assertion: neural network | /r/HasProperty | linear function
Facets:
value: without this
type: OTHER
value: at the
type: LOCATION
value: in the parameters
type: OTHER
value: essentially
type: DEGREE
value: in simple cases
type: TEMPORAL
Source sentences:
text: It can be a gaussian function, logistic function, hyperbolic function or even a linear function in simple cases.
text: A neural network without an activation function is essentially just a linear regression model.
text: Now, ignoring what issues this neural network has, it should be clear, that at the very least, it is non-linear in the parameters and that non-linearity has been introduced solely by choice of the activation function.
text: So, if didn’t have non-linear function applied in each neuron, the neural network would be a linear function, thus not more powerful than a single neuron.
text: Without this, a neural network would be just a linear regression model.