Neural Networks

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<math>[-1,1]</math> instead of <math>[0,1]</math>.
<math>[-1,1]</math> instead of <math>[0,1]</math>.
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Note that unlike CS221 and (parts of) CS229, we are not using the convention
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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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node).  The middle layer of nodes is called the '''hidden layer''', because its
node).  The middle layer of nodes is called the '''hidden layer''', because its
values are not observed in the training set.  We also say that our example
values are not observed in the training set.  We also say that our example
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neural network has 3 '''in put units''' (not counting the bias unit), 3  
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neural network has 3 '''input units''' (not counting the bias unit), 3  
'''hidden units''', and 1 '''output unit'''.
'''hidden units''', and 1 '''output unit'''.
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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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%We will write <math>\textstyle s_l</math> to denote the
 
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%number of units in layer <math>\textstyle l</math> of the network (not counting the bias unit).
 
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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}}

Latest revision as of 19:38, 6 April 2013

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