# [C2W1L4] Intuition 1 behind Regularization

For intuition 1, Andrew said that " If `lambda` is too large - a lot of weights will be close to zeros which will make the NN simpler (you can think of it as it would behave closer to logistic regression). "

Why when a lot of weights will be close to zeros, the NN will be behave close to logistic regression?
Thanks

If W (weights) are close to 0 then g(Wx+b) is almost g(b), the sigmoid of a bias term, which as we move deeper to the neural network becomes the computation of sigmoid of a previous number, because the weights are negligible. So basically its a just the sigmoid of number as far as i understand it.

1 Like

so what Andrew meant is because a lot of neurons will shut down, the NN will be simpler as if it is "a logistic regression NN (because logistic regression NN has one layer )?

That means â€śyou can think of it as it would behave closer to logistic regressionâ€ť is just to make us visualize how simple the NN will be ?

Thanks

Yeah i think thats right what you say.