Autoencoders and Sparsity
From Ufldl
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So far, we have described the application of neural networks to supervised learning, in which we have labeled | So far, we have described the application of neural networks to supervised learning, in which we have labeled | ||
| - | training examples. Now suppose we have only unlabeled training examples | + | training examples. Now suppose we have only a set of unlabeled training examples <math>\textstyle \{x^{(1)}, x^{(2)}, x^{(3)}, \ldots\}</math>, |
where <math>\textstyle x^{(i)} \in \Re^{n}</math>. An | where <math>\textstyle x^{(i)} \in \Re^{n}</math>. An | ||
'''autoencoder''' neural network is an unsupervised learning algorithm that applies backpropagation, | '''autoencoder''' neural network is an unsupervised learning algorithm that applies backpropagation, | ||
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<math>\textstyle J_{\rm sparse}(W,b)</math>. Using the derivative checking method, you will be able to verify | <math>\textstyle J_{\rm sparse}(W,b)</math>. Using the derivative checking method, you will be able to verify | ||
this for yourself as well. | this for yourself as well. | ||
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| + | {{Sparse_Autoencoder}} | ||
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| + | {{Languages|自编码算法与稀疏性|中文}} | ||
