Assertion: neural network's neuron | /r/CapableOf | receive input
Facets:
value: without a bias neuron
type: OTHER
value: from the first layer
type: OTHER
value: with two differences
type: OTHER
value: in simple terms
type: MANNER
value: from groups of primary receptors
type: OTHER
value: in a convolutional layer
type: LOCATION
value: in a fully connected layer
type: MANNER
value: in neural networks
type: OTHER
value: to its left
type: LOCATION
value: in both networks
type: LOCATION
Source sentences:
text: Every neuron connected in the network receives an input and performs a dot product on it.
text: Here, a neuron will take an input, apply some activation function to it, and generate an output.
text: The artificial neuron could take in an input, process it based on some rules, and fire a result.
text: The artificial neuron receives one or more inputs (representing dendrites) and sums them to produce an output.
text: Without a bias neuron, each neuron takes the input and multiplies it by a weight, with nothing else added to the equation.
text: Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output.
text: In both networks the neurons receive some input, perform a dot product and follows it up with a non-linear function like ReLU(Rectified Linear Unit).
text: Each neuron in the first hidden layer receives all the inputs from the first layer.
text: Each neuron accepts part of the input and passes it through the activation function .
text: An artificial neuron receives an input.
text: Neurons will receive an input from predecessor neurons that have an activation , threshold , an activation function f, and an output function .
text: In neural networks, each neuron receives input from some number of locations in the previous layer.
text: In a fully connected layer, each neuron receives input from every element of the previous layer.
text: In a convolutional layer, neurons receive input from only a restricted subarea of the previous layer.
text: Second-level visual neurons receive input from groups of primary receptors, higher-level neurons receive input from groups of second-level neurons, and so on, forming a hierarchy of processing stages.
text: Each neuron receives inputs from several other neurons, multiplies them by assigned weights, adds them and passes the sum to one or more neurons.
text: Each neuron receives inputs from the neurons to its left, and the inputs are multiplied by the weights of the connections they travel along.
text: Each neuron receives inputs from several other neurons, multiplies them by assigned weights, adds them and passes the sum to one or more neurons.
text: All neurons (at each layer) of a TDNN receive inputs from the outputs of neurons at the layer below but with two differences:
text: Every neuron receives some inputs, performs a dot product, and sometimes follow non-linearity.
text: Each neuron receives some inputs, performs a dot product and optionally follows it with a non-linearity.
text: Each neuron takes an input, performs some operation on them (calculates z = w[T] + b), and then applies the sigmoid function:
text: A neural network is a collection of neurons that take input and, in conjunction with information from other nodes, develop output without programmed rules.
text: In simple terms, each neuron takes input from numerous other neurons through the dendrites.
text: Each neuron of each layer is just a simple perceptron that takes input from the previous layer, does some sort of calculation and provides the output to the next layer.
text: A neuron takes inputs, does some math with them, and produces one output.
text: A neuron takes inputs, does some math with them, and produces one output.
text: Each neuron takes in an input from one-or-more neurons along with the weights and a bias.
text: Each neuron takes in an input from one-or-more neurons along with the weights and a bias which I will explain in detail later on.
text: The artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites) and sums them to produce an output (or activation, representing a neuron's action potential which is transmitted along its axon).
text: The artificial neurons of one or more layers receive one or more inputs (representing dendrites), and after being weighted, sum them to produce an output (representing a neuron's axon).
text: Without a bias neuron, each neuron can only take the input and multiply it by a weight.
text: Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output.