Yamale
Regularization xeetu pexe la buy tënk benn model ci anam wu jaar yoon, suko defee mu mëna jëfandikoo ay done yu bees ci barabu xam limu tàggat ci boppam.
Résumé
It is the main toolkit for fighting overfitting.
Plongeur bu xóot
Suñu ko bàyyeewul, xeetu model bu yomb bi dina wëlbatiku ngir mëna ànd ak bépp poñ ci done yiñ tàggat, ba ci bruit bi. Regularisation dafay delloo ginaaw ci yokk penalti wala constraint buy jàppale pexe yu gëna yomba def. Formu yiñ gëna xam ñooy yokk benn term ci fonction perte bi lalu ci dayo poid yi ci model bi. L2 regularisation (decay poids) dafay yar poids yu mag yi ci anam wu yomb, di leen wàññi ba zero ba noppi defar ay model yu gëna nooy. L1 regularisation dafay yar valeur absolu bu poid yi te mën na yóbbu yenn ci zero, di tànnee ci anam wu baax benn subset ci màndarga yi. Lu weesu daan yi, dropout dafay fay neurons yi ci diiru tàggat yaram, teel taxaw dafay taxawal tàggat yaram balaa overfitting di dugg, ba noppi yokk done yi dafay yaatal tàggat yaram bu baax. Ku nekk ci ñoom defay wecci tuuti ci taggat yaram ngir gëna am doole ci àdduna bi.
Gis-gis xarala
Regularisation yu bari dañuy soppi mébet bi optimiser bi di wàññi. Duñu wàññi njuumte yi ci wax luy waaja am, waaye dañuy wàññi njuumte yi boole ci lambda yoon penalti ci poid yi, fu lambda di saytu doole ji. L2 dafay yokk limu poid kaare yi, di ñaax poid yu ndaw yu bari; L1 dafay yokk limu poid absolu yi, di ñaax sparsity ak zero exact. Dropout dafay dox ci anam wu wuute: ci zeroing activations ci jéego bu nekk, dafay tere neurons yi co-adapter ak jege tàggat ensemble subnetworks. Loolu lépp dafay wàññi variance ci njëgu yokk tuuti bias.
njeextalu pexe
dogal yu gëna leer
Daf lay jàppale nga tàqale kàddu yu leer ci wàllu xarala ak làkku fësal njaay.
Njëgg ak budget
Mën nga laaj laaj yu gëna baax ci samp gi balaa ngay dugal xaalis wala sa jotu liggéey.
Ekip ak def liggéey
Ekip yi bokk xam-xam ñoo gëna mëna jël yenn dogal ci wàllu produit, politik ak jàng.
Ëlëgu Regularisasioŋ
Daan yu leer yu melni L2 ak dropout ñu ngi wéy di nekk, waaye xel mi mingi toxu ci regularisation bu nëbbu, anam wi optimisateurs yu melni stochastic gradient descent di jaay model yu mag yi ci pexe yuñ mëna jëfandikoo doonte duñu yokk benn daan. Pexe yu melni etiketu nooy, jaxase, ak yokk done yu gëna am doole ñu ngi gëna am solo ci tàggat gis-gis bu yaatu ak misaali làkk. Xaarandi yeneen gëstu ci li waral reso yu bari parametre baña jëfandikoo lu ëpp, ak ci pexe yuy méngoo yuy méngale dooley regularisation ci saasi ci diiru tàggat yaram moo gën ñuy yéem ci seetlu loxo.
Doxal ci àdduna dëgg
Yokk L2 poid decay ci classifier nataal bu xóot suko defee mu generalise ci ay junni nataali tàggat yaram ba ci yi ñu gisul.
Jëfandikoo L1 regularisation ci benn model genomics ngir tann ci saasi gene yu néew yiy wax luy am ci ay junni junni.
Jëfandikoo dropout ci reso recommande suko defee mu baña yéem benn siñaal jëfandikukat.
Teela taxawal tàggat yaram ginaaw bi ñàkka am validation taxawee di yokk, doonte ñàkka tàggat yaram mën na wéy di wàññeeku.
Risk yi ak balustrade yi
Ekip yu bari mën nañu jëfandikoo benn baat ci anam wu wuute, kon teela leeral yaatuwaayam.
Benchmark yi mën nañu nuru lu am doole waaye performance yi ci àdduna bi duñu tolloo.
Bëgg kalite done ak palaŋu jàngat dafay faral di jur njariñ yu yomba dagg.
Roadmap ngir samp gi
Tàmbaleel ci joxe leeral ci làkk wu leer ci njariñ li nga soxla.
Tannal benn metric bu baax ak benn anam bu baaxul balaa ngay saytu.
Doxal ab pilote bu ndaw ak ay done yu representatif, du ab demo bu leer.
Bindal fi Regularization di jàppale ak fi pexe yu gëna yomba gëna baax.
Weyal di banneexu
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Gis bi ci topp
Decay de poids et régulation L2
Laaj yi ñuy faral di laaj
What is Regularization?
Regularization xeetu pexe la buy tënk benn model ci anam wu jaar yoon, suko defee mu mëna jëfandikoo ay done yu bees ci barabu xam limu tàggat ci boppam. Mooy jumtukaay bi gëna am solo ngir xeex overfitting.
Lan mooy mébetu yamale gi?
Regularization dafay tere benn model ngir tere nit ñi xam done yi ñuy tàggat, gëna baaxal performance ci misaal yuñu gisul.
Ban pexem yamale moo gëna mëna yóbbu yenn poid yi ci nul, di tànnee ci anam wu jaar yoon?
L1 dafay yar valeur absolu bu poid yi, te loolu dafay push poid yu gëna néew njariñ ci zero ndànk, def tànneef ci saasi. L2 dafay wàññi poid yi bu baax waaye bariwul lumuy wàññi ndànk.
naka lay demee ba bàyyi njàng mu mëna yamale reso neuronal bi ci diiru tàggat yaram?
Dropout dafay zero ci anam wu bari benn wàll ci activations ci jéego bu nekk ci tàggat yaram, di tere neurons yi di yéem seen biir lu bari te jegesi ensemble subnetworks.
Ci perte buñ yamale 'njuumte + lambda × daan', lan la yokk lambda di def?
Lambda mooy saytu dooley yamale gi. Lambda bu gëna mag dafay jox penalti bi gëna am doole, di wàññi poids yi gëna am doole ci model yu gëna yomba def.
Lan moo waral ñu jàpp ni teela taxaw dafay nuru yamale?
Taxawal su performance validation taxawee yokkute dafay tere model bi wéy ci regime bi muy memorise bruit, lu melni yeneen regularisers di tënk complexité.