Translations:Neural Networks/14/en: Difference between revisions

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    where <math>g</math> and <math>f</math> are {{Term|activation function|activation functions}}, <math>\mathbf{W}_1, \mathbf{W}_2</math> are weight matrices, and <math>\mathbf{b}_1, \mathbf{b}_2</math> are bias vectors. The hidden layer enables the network to learn nonlinear relationships that a single perceptron cannot capture.
    where <math>g</math> and <math>f</math> are activation functions, <math>\mathbf{W}_1, \mathbf{W}_2</math> are weight matrices, and <math>\mathbf{b}_1, \mathbf{b}_2</math> are bias vectors. The hidden layer enables the network to learn nonlinear relationships that a single perceptron cannot capture.

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    Message definition (Neural Networks)
    where <math>g</math> and <math>f</math> are {{Term|activation function|activation functions}}, <math>\mathbf{W}_1, \mathbf{W}_2</math> are weight matrices, and <math>\mathbf{b}_1, \mathbf{b}_2</math> are bias vectors. The hidden layer enables the network to learn nonlinear relationships that a single perceptron cannot capture.

    where $ g $ and $ f $ are activation functions, $ \mathbf{W}_1, \mathbf{W}_2 $ are weight matrices, and $ \mathbf{b}_1, \mathbf{b}_2 $ are bias vectors. The hidden layer enables the network to learn nonlinear relationships that a single perceptron cannot capture.

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