Difference between revisions of "Regularization"
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Revision as of 09:10, 30 December 2018
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Different Regularization techniques in Deep Learning:
- L2 and L1 regularization
- Dropout
- Data augmentation
- Early stopping
Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model’s performance on the unseen data as well. An Overview of Regularization Techniques in Deep Learning (with Python code) | Shubham Jain
