Neural Networks
From Ufldl
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- | + | <div align=center> | |
- | [[Image:Sigmoid_Function.png|400px| | + | [[Image:Sigmoid_Function.png|400px|top|Sigmoid activation function.]] |
- | [[Image:Tanh_Function.png|400px| | + | [[Image:Tanh_Function.png|400px|top|Tanh activation function.]] |
+ | </div> | ||
The <math>\tanh(z)</math> function is a rescaled version of the sigmoid, and its output range is | The <math>\tanh(z)</math> function is a rescaled version of the sigmoid, and its output range is | ||
<math>[-1,1]</math> instead of <math>[0,1]</math>. | <math>[-1,1]</math> instead of <math>[0,1]</math>. | ||
- | Note that unlike | + | Note that unlike some other venues (including the OpenClassroom videos, and parts of CS229), we are not using the convention |
here of <math>x_0=1</math>. Instead, the intercept term is handled separately by the parameter <math>b</math>. | here of <math>x_0=1</math>. Instead, the intercept term is handled separately by the parameter <math>b</math>. | ||
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example of a '''feedforward''' neural network, since the connectivity graph | example of a '''feedforward''' neural network, since the connectivity graph | ||
does not have any directed loops or cycles. | does not have any directed loops or cycles. | ||
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patient, and the different outputs <math>y_i</math>'s might indicate presence or absence | patient, and the different outputs <math>y_i</math>'s might indicate presence or absence | ||
of different diseases.) | of different diseases.) | ||
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+ | {{Sparse_Autoencoder}} | ||
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+ | {{Languages|神经网络|中文}} |