quiz bu lëkkale ak tegtal · Diggante Niveau
Quiz buy yamale
Saytu mébetu regularisasioŋ, L1 ak L2 penalti, dropout, teela taxaw, dooley penalti, ak misaali lim ci mébet buñ yamale.
Laaj 1 de 8
Why would a practitioner add regularization when a model fits training-specific noise?
Xoolaatal laaj bu nekk8 laaj
Sooy def quiz bi, tontu yi ak leeral yi dañuy feeñ.
- Why would a practitioner add regularization when a model fits training-specific noise?
- To guarantee zero training error
- To remove the need for validation data
- To guarantee a faster GPU kernel
- To discourage overfitting and evaluate whether unseen-data performance improves
- Ban pexem yamale moo gëna mëna yóbbu yenn poid yi ci nul, di tànnee ci anam wu jaar yoon?
- L2 regularisasioŋ (degradasioŋ poids)
- L1 yamale
- Normalisasioŋ ci lots
- Yokk limu jàng
- naka lay demee ba bàyyi njàng mu mëna yamale reso neuronal bi ci diiru tàggat yaram?
- Dafay efaase ay couche ci reso bi ba fàww
- Dafay yokk poid kaare yi ci fonction perte bi
- Dafay fay neuron yi jéego bu nekk suko defee ñu baña mëna ànd
- Dafay taxawal tàggat yaram su perte validation yokke
- Ci perte buñ yamale 'njuumte + lambda × daan', lan la yokk lambda di def?
- Dafay gëna dooleel penalti bi, di puus model bi ci pexe yu gëna yomb
- Dafay néewal doole penalti bi ba noppi may poid yu gëna rëy
- Dindil term njuumte bi yépp
- Amul benn njeexital ci diisaay yi
- Lan moo waral ñu jàpp ni teela taxaw dafay nuru yamale?
- Ndax dafay yokk poids ci perte bi
- Ndax dafay taxawal tàggat yaram balaa model bi di tàmbali def bruit ci done yi
- Ndax dafay yokk kàttanu model bi ci diir bi
- Ndax dafay dindi neuron yi ci anam wu mujjee
- What can happen when regularization is strengthened beyond what a task needs?
- Both training and test accuracy must improve
- The penalty becomes unrelated to the objective
- Validation becomes unnecessary
- The model can underfit useful structure even if variance decreases
- For a regularization example with weights minus two and one, what are the unscaled L1 and sum-of-squares L2 penalties?
- L1 is three and L2 is five
- L1 is minus one and L2 is three
- L1 is five and L2 is three
- Both penalties are minus two
- A regularized objective has prediction error two, lambda one-half, and penalty six. What is its total value?
- Juróom ñatt
- Ñatt
- Juroom
- Benn