Assertion: neural network's neuron | /r/HasAspect | weight
Facets:
value: typically
type: DEGREE
Source sentences:
text: Neurons and edges typically have a weight that adjusts as learning proceeds.
text: Each of these layers contains neurons that are connected to neurons in the previous layer, and each neuron has its own weight.
text: Remember this: each neuron has an activation a and each neuron that is connected to a new neuron has a weight
text: Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream.
text: Neurons and edges typically have a weight that adjusts as learning proceeds.
text: Artificial neurons and edges typically have a weight that adjusts as learning proceeds.
text: It contains neutrons, and each neuron in it is given an input and has a weight.
text: If the receptive field (or the filter size) is 5x5, then each neuron in the Conv Layer will have weights to a [5x5x3] region in the input volume, for a total of 5*5*3 = 75 weights (and +1 bias parameter).