Exercise:Self-Taught Learning

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(Support Code/Data)
(Step 3: Extracting features)
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After the sparse autoencoder is trained, we can use it to extract features from the handwritten digit images.  
After the sparse autoencoder is trained, we can use it to extract features from the handwritten digit images.  
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Complete <tt>feedForwardAutoencoder.m</tt> to produce a matrix whose columns correspond to activation of the hidden layer for each example i.e. the vector <math>a^{(2)}</math> corresponding to activation of layer 2.
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Complete <tt>feedForwardAutoencoder.m</tt> to produce a matrix whose columns correspond to activation of the hidden layer for each example i.e. the vector <math>a^{(2)}</math> corresponding to activation of layer 2 (recall that we treat the inputs as layer 1).
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After doing so, this step will use your modified function to convert the raw image data to feature unit activations.  
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After doing so, this step will use your modified function to convert the raw image data to feature unit activations.
===Step 4: Training and testing the logistic regression model===
===Step 4: Training and testing the logistic regression model===

Revision as of 09:27, 8 May 2011

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