Self-Taught Learning to Deep Networks
From Ufldl
(→Feature Learning pipeline) |
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training data, this can significantly improve your classifier's performance. | training data, this can significantly improve your classifier's performance. | ||
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In self-taught learning, we first trained a sparse autoencoder on the unlabeled data. Then, | In self-taught learning, we first trained a sparse autoencoder on the unlabeled data. Then, | ||
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as part of the sparse autoencoder training process. The second layer | as part of the sparse autoencoder training process. The second layer | ||
of weights <math>\textstyle W^{(2)}</math> mapping from the activations <math>\textstyle a</math> to the output <math>\textstyle y</math> was | of weights <math>\textstyle W^{(2)}</math> mapping from the activations <math>\textstyle a</math> to the output <math>\textstyle y</math> was | ||
- | trained using logistic regression (or softmax regression). | + | trained using logistic regression (or softmax regression). |
== Fine-tuning == | == Fine-tuning == |