Neural Network Nodes - Deep Learning Dictionary
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Neural Network Nodes/Neurons - Deep Learning Dictionary
An artificial neural network is made up of multiple processing units called nodes or neurons that are organized into layers. These layers are connected to each other via weights.
The number of nodes in each layer depends partly on where the layer resides in the network, partly on the data that will eventually be passed to the layer, and partly on network architect's design choice for layer.
| Layer | Number of Nodes |
|---|---|
| Input layer | Must have one node for each component/feature of the input data |
| Hidden layers | Number of nodes is chosen (arbitrarily or empirically) by the network architect |
| Output layer | Must have one node for each of the possible output classes |
The nodes in the network are simply processing units for data that will eventually be passed to them.
Each node has an output value that is the result of some operation that occurs between the data that is passed to it as input, along with the weights that connect the input to the node. The operation depends on the type of layer for which the node is part of.
The output from nodes in one layer are passed as input to nodes in the next layer.
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