Difference between revisions of "Early Stopping"
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| − | [ | + | {{#seo: |
| − | [ | + | |title=PRIMO.ai |
| + | |titlemode=append | ||
| + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS | ||
| + | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
| + | }} | ||
| + | [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] | ||
| − | Good practices for addressing | + | * [https://en.wikipedia.org/wiki/Early_stopping Early Stopping | Wikipedia] |
| + | |||
| + | Good practices for addressing the [[Overfitting Challenge]]: | ||
* add more data | * add more data | ||
| − | * use [[Data | + | * use [[Data Quality#Batch Norm(alization) & Standardization|Batch Norm(alization) & Standardization]] |
| − | |||
* use architectures that generalize well | * use architectures that generalize well | ||
* reduce architecture complexity | * reduce architecture complexity | ||
* add [[Regularization]] | * 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) |
| − | ** [[Data Augmentation]] | + | ** [[Data Augmentation, Data Labeling, and Auto-Tagging|Data Augmentation]] |
| − | ** Early Stopping | + | ** [[Early Stopping]] |
<youtube>7QfUNxkthq8</youtube> | <youtube>7QfUNxkthq8</youtube> | ||
<youtube>ATuyK_HWZgc</youtube> | <youtube>ATuyK_HWZgc</youtube> | ||
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
