Difference between revisions of "Early Stopping"
| Line 9: | Line 9: | ||
* add more data | * add more data | ||
* use [[Data Augmentation]] | * use [[Data Augmentation]] | ||
| − | * use [[Batch | + | * use [[Batch Norm(alization) & Standardization]] |
* use architectures that generalize well | * use architectures that generalize well | ||
* reduce architecture complexity | * reduce architecture complexity | ||
Revision as of 19:32, 2 January 2019
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Good practices for addressing the Overfitting Challenge:
- add more data
- use Data Augmentation
- use Batch Norm(alization) & Standardization
- use architectures that generalize well
- reduce architecture complexity
- add Regularization
- L1 and L2 Regularization - update the general cost function by adding another term known as the regularization term.
- Dropout - at every iteration, it randomly selects some nodes and temporarily removes the nodes (along with all of their incoming and outgoing connections)
- Data Augmentation
- Early Stopping
