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== Regularization in Deep Learning == | <!--== Regularization in Deep Learning == | ||
=== Introduction === | === Introduction === | ||
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*<math>x*{g}=\sum_{i}\theta_iUT_i(\tilde{\Alpha})U^Tx</math> | *<math>x*{g}=\sum_{i}\theta_iUT_i(\tilde{\Alpha})U^Tx</math> | ||
*<math>x*{g_{\theta}}=U\left(\sum_{i}\theta_iT_i(\tilde{\Alpha})\right)U^Tx</math> | *<math>x*{g_{\theta}}=U\left(\sum_{i}\theta_iT_i(\tilde{\Alpha})\right)U^Tx</math> | ||
*This is equal to: <math>x*{g_{\theta}}=\sum_{i=1}^k{\theta_iT_i(\tilde{L})x}</math>, where <math>\tilde{L}=\frac{2L}{\lambda_{max}}-I_n</math> | *This is equal to: <math>x*{g_{\theta}}=\sum_{i=1}^k{\theta_iT_i(\tilde{L})x}</math>, where <math>\tilde{L}=\frac{2L}{\lambda_{max}}-I_n</math> --> |