Difference between revisions of "Regularization"
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* [[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) | ||
* [[Data Augmentation]] | * [[Data Augmentation]] | ||
| − | * Early Stopping | + | * [[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. [http://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/ An Overview of Regularization Techniques in Deep Learning (with Python code) | Shubham Jain] | 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. [http://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/ An Overview of Regularization Techniques in Deep Learning (with Python code) | Shubham Jain] | ||
Revision as of 16:18, 30 December 2018
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Different Regularization techniques:
- L2 and L1 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
