What are some effective solutions to prevent overfitting in AI training?

What are some effective solutions to prevent overfitting in AI training?

There are several strategies to prevent overfitting, such as using regularization techniques, reducing model complexity, and employing dropout.

Can you explain what regularization techniques are?

Regularization techniques add a penalty to the loss function to discourage overly complex models. Common methods include L1 and L2 regularization.

What is dropout and how does it help?

Dropout is a technique where randomly selected neurons are ignored during training, which helps prevent the model from becoming too reliant on any specific feature.

Are there any data augmentation techniques that can help?

Yes, data augmentation techniques like rotation, scaling, and flipping can increase the diversity of your training data, helping to reduce overfitting.

How can I know if my model is overfitting?

You can monitor the training and validation loss. If the training loss decreases while the validation loss increases, your model is likely overfitting.