Assertion: neural network's neuron | /r/HasProperty | activated
Facets:
value: by calculating the weighted sum
type: MANNER
Source sentences:
text: If the signals passes through, the neuron has been “activated.
text: Activation function decides whether a neuron should be activated or not by calculating the weighted sum and further adding bias to it.
text: When a neuron is activated, it produces a signal that is passed to connected neurons, raising or lowering their membrane potential.
text: It turned out that diagonal edges at a very particular angle were causing certain neurons to be activated.
text: But..since there are intermediate activation values for the output, learning can be smoother and easier ( less wiggly ) and chances of more than 1 neuron being 100% activated is lesser when compared to step function while training ( also depending on what you are training and the data ).
text: Activation function decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias with it.
text: Activation function decides whether a neuron should be activated or not by calculating the weighted sum and further adding bias to it.