What is a neuron?
In machine learning, a distinct unit within a hidden layer of a neural network.
neuron explained in plain English
In machine learning, a distinct unit within a hidden layer of a neural network. Each neuron performs the following two-step action: 1. Calculates the weighted sum of input values multiplied by their corresponding weights. 2. Passes the weighted sum as input to an activation function. A neuron in the first hidden layer accepts inputs from the feature values in the input layer. A neuron in any hidden layer beyond the first accepts inputs from the neurons in the preceding hidden layer. For example, a neuron in the second hidden layer accepts inputs from the neurons in the first hidden layer. The following illustration highlights two neurons and their inputs. A neuron in a neural network mimics the behavior of neurons in brains and other parts of nervous systems.
Example
Practitioners refer to neuron when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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- activation function
A function that enables neural networks to learn nonlinear (complex) relationships between features and the label.
- Backpropagation
The process that tells a neural network which internal settings caused an error and how to adjust them, working backwards through layers.
- batch
The set of examples used in one training iteration.
- batch normalization
Normalizing the input or output of the activation functions in a hidden layer.
- batch size
The number of examples in a batch.
- Bayesian neural network
A probabilistic neural network that accounts for uncertainty in weights and outputs.
- co-adaptation
An undesirable behavior in which neurons predict patterns in training data by relying almost exclusively on outputs of specific other neurons instead of relying on the network's behavior as a whole.
- convergence
A state reached when loss values change very little or not at all with each iteration.
- deep model
A neural network containing more than one hidden layer.
- depth
The sum of the following in a neural network: - the number of hidden layers - the number of output layers, which is typically 1 - the number of any embedding layers For example, a neural network with five hidden layers and one output layer has a depth of 6.