Difference between revisions of "Regularization"
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* use architectures that generalize well | * use architectures that generalize well | ||
* reduce architecture complexity | * reduce architecture complexity | ||
| − | * add | + | * add Regularization |
** [[L1 and L2 Regularization]] - update the general cost function by adding another term known as the regularization term. | ** [[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) | ** [[Dropout]] - at every iteration, it randomly selects some nodes and temporarily removes the nodes (along with all of their incoming and outgoing connections) | ||
Revision as of 17:04, 30 December 2018
Youtube search... ...Google search
Good practices for addressing the Overfitting Challenge:
- add more data
- use Data Augmentation
- use Batch Normalization
- 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
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
