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About Deep Learning Basics Lecture 3 Regularization I
Regularization helps prevent overfitting by adding additional terms or constraints to the training objective. It can be viewed as imposing hard constraints during optimization, adding regularization terms like l2 or l1 norms as soft constraints, or incorporating priors over the parameters in a Bayesian view. L2 regularization scales parameter values proportionally along eigenvectors of the Hessian, while l1 regularization induces sparsity by driving small parameter values to exactly zero.
Detailed Information
| Author: | ['baris'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 32 |
| Language: | English |
| Format: | |
| Price: | FREE |
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