Difference between revisions of "Early Stopping"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=Early+Stopping+Regularization+Dropout+Overfitting Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=Early+Stopping+Regularization+Dropout+deep+machine+learning+ML ...Google search] |
| − | * [ | + | * [https://en.wikipedia.org/wiki/Early_stopping Early Stopping | Wikipedia] |
Good practices for addressing the [[Overfitting Challenge]]: | Good practices for addressing the [[Overfitting Challenge]]: | ||
Latest revision as of 10:14, 28 March 2023
Youtube search... ...Google search
Good practices for addressing the Overfitting Challenge:
- add more data
- 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
