Neural Networks: Difference between revisions
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=Individual Neuron= | =Individual Neuron= | ||
Individual neurons are computational units that read ''input features'', represented as an unidimensional vector x1 ... xn in the diagram below, and calculate the hypothesis function as ''output''. | Individual neurons are computational units that read ''input features'', represented as an unidimensional vector x1 ... xn in the diagram below, and calculate the hypothesis function as ''output''. Note that x0 is not part of the feature vector, but it represents a bias value for the unit. | ||
[[Image:Neuron.png]] | [[Image:Neuron.png]] | ||
Revision as of 02:32, 4 January 2018
Internal
Individual Neuron
Individual neurons are computational units that read input features, represented as an unidimensional vector x1 ... xn in the diagram below, and calculate the hypothesis function as output. Note that x0 is not part of the feature vector, but it represents a bias value for the unit.
This is a representation of a logistic unit with a sigmoid (logistic) activation function.
The θ vector represents the model's parameters (model's weights). For a multi-layer neural network, the model parameters are collected in matrices named Θ, which will be describe below.
The bias unit is optional, but when it is provided, it is always 1.
Multi-Layer Neural Network
When the output