Why can the number of iterations only increase so far when utilizing the waterfall model?

Using Matlab, plot the following ultrasound pressure wave as a function of x for t =1 P(t, x) = Poe** cos(@t – kx) where a = 0.15 neper/m, where neper is a dimensionless quantity, k = 1 rad/cm, o = 1 rad/s, and Po = 15 N/m2. Submit your ".m" file as well as the resulting graph labeled appropriately.

Write the code in matlab.

Computer Science A way to avoid overfitting in Deep Neural Networks is to add an additional term R to the loss function L (for example L can be the cross entropy loss) as follows: L(w) + λR(w). (1) You know that one choice for R is the L2 norm, i.e. R(w) = ||w||2 2 . One friend of yours from the School of Maths told you however that there’s no need to use squares (i.e. powers of two) and that you can achieve the same effect by using absolute values, i.e. the L1 norm: R(w) = ||w||1. Would you agree with him? i.e. is the use of the L2 norm equivalent to using the L1 norm for regularization purposes? Justify your answer