How does increasing the neural networks layers (making it bigger) help reduce the bias?

I can understand that reducing regularization parameter, adding more features can help reduce the bias and how they do so. But i can’t grasp how a bigger neural network deals with bias

Bigger NN’s can learn more complex models. So that reduces bias (i.e. they’re more likely to overfit rather than underfit).

Hello @Jay_01,

As though reducing regularization parameter and adding more features can help with reducing the bias, but in the other hand, it could increase the chance of overfitting.
So as @TMosh mentioned, bigger neural network could lead to smaller bias but the efficiency of the neural net and knowing the problem is more important than just adding layers.

Cheers,
Amir

That was silly of me, thank you for your explanations.