Self-Taught Learning to Deep Networks
From Ufldl
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features <math>\textstyle a</math>. This is illustrated in the following diagram: | features <math>\textstyle a</math>. This is illustrated in the following diagram: | ||
| - | [[File:STL_SparseAE_Features.png| | + | [[File:STL_SparseAE_Features.png|300px]] |
We are interested in solving a classification task, where our goal is to | We are interested in solving a classification task, where our goal is to | ||
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To illustrate this step, similar to [[Neural Networks|our earlier notes]], we can draw our logistic regression unit (shown in orange) as follows: | To illustrate this step, similar to [[Neural Networks|our earlier notes]], we can draw our logistic regression unit (shown in orange) as follows: | ||
| - | [[File:STL_Logistic_Classifier.png| | + | ::::[[File:STL_Logistic_Classifier.png|380px]] |
Now, consider the overall classifier (i.e., the input-output mapping) that we have learned | Now, consider the overall classifier (i.e., the input-output mapping) that we have learned | ||
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only a relatively small labeled training set, then fine-tuning is significantly less likely to | only a relatively small labeled training set, then fine-tuning is significantly less likely to | ||
help. | help. | ||
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| + | {{CNN}} | ||
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| + | {{Languages|从自我学习到深层网络|中文}} | ||
