Assertion: neural network's function | /r/HasProperty | linear
Facets:
value: typically
type: DEGREE
value: for values
type: PURPOSE
Source sentences:
text: It can be a gaussian function, logistic function, hyperbolic function or even a linear function in simple cases.
text: This combination of weighted input signals and the functions applied are typically either linear or nonlinear.
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: 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: The function f is non-linear and is called the Activation Function.
text: This combination of weighted input signals and the functions applied are typically either linear or nonlinear.
text: The function can be either linear or nonlinear.
text: As you can see the function is a line or linear.
text: The function is linear for values greater than zero, meaning it has a lot of the desirable properties of a linear activation function when training a neural network using backpropagation.
text: Because the rectified function is linear for half of the input domain and nonlinear for the other half, it is referred to as a piecewise linear function or a hinge function.
text: The function f is non-linear and is called the Activation Function.
text: The function can be either linear or nonlinear.
text: — our only purpose here is to show that even a simple neural network can learn a nonlinear function.
text: On each hidden layer, the neural network learns new feature space by first compute the affine (linear) transformations of the given inputs and then apply non-linear function which in turn will be the input of the next layer.