How to define the termination condition for gradient descent?

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

4. (1) Draw a transition graph for the dfa M={Q,E,8,q,,F ), where Q={q,,91»92 }; E = {a,b},F = {qo,92} and ở is definded as S(90,a) = q,,8(q,b) =q,,5(q,,a)=q0,8(q,,b) =q2,8(q2,a) =q2,8(q2,b) =q2 (2) Give the language accepted by the above dfa.

Which SDLC phases does the cascade method cover? This may be the source of Barry Boehm's results.

Simplify the following with Identities and K-Maps: xy~z + ~x~yz + yzPlease explain all steps

175. Bradac found that linear nature of waterfall model leads to a. flowing state b. blocking state c. dynamic state d. random state

N.B : Course Title: Neural Network & Fuzzy Logic