What are negative weights in neural networks?

What are negative weights in neural networks?

Negative weights in neural networks are values that can decrease the output of a neuron, allowing for more complex decision boundaries.

How do negative weights affect the learning process?

Negative weights can help the model learn to recognize patterns by allowing it to adjust the influence of certain inputs, enhancing its ability to generalize.

Can you give an example of when negative weights are useful?

Sure! In a binary classification task, negative weights can help the model differentiate between classes by reducing the influence of certain features that are not helpful.

Are there any drawbacks to using negative weights?

Yes, if not managed properly, negative weights can lead to overfitting, where the model learns noise in the training data instead of the underlying pattern.