Neural Networks
From Ufldl
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<math>[-1,1]</math> instead of <math>[0,1]</math>. | <math>[-1,1]</math> instead of <math>[0,1]</math>. | ||
- | Note that some other venues (including the OpenClassroom videos, and parts of CS229), we are not using the convention | + | 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 | ||
- | neural network has 3 ''' | + | 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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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}} |