InoteveraGaidhi rinotevera
Linear Algebra for Machine Learning
Basics
Basics GUIDE
Active Learning
Kudzidzira kunoshanda inzira yekudzidzisa apo modhi pachayo inosarudza kuti ndeipi mienzaniso isina kunyorwa iyo munhu anofanira kunyora inotevera.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Pa peji ino2 min verenga
Pfupiso
It matters because labeling data is expensive, and smart selection can reach high accuracy with a fraction of the annotations.
Kudzika Kwakadzika
Kunyanya kutariswa kudzidza kunofungidzira kuti watove uine murwi mukuru we data rakanyorwa. Kudzidzira kunoshanda kunotenderedza kuti: unotanga nediki rakanyorwa seti uye dziva hombe remienzaniso isina kunyorwa, wobva wabvunza kasingaperi munhu ('iyo 'oracle') kuti anyore iyo inodzidzisa chete. Iyo modhi inodzidziswa, inoshandiswa kukora dziva risina kunyorwa, uye mienzaniso yepamusoro-soro inotumirwa kuti inyore - ipapo loop inodzokorora. Mazano ekusarudza akajairwa anosanganisira kusavimbika sampling (sarudza mienzaniso iyo modhi isina chivimbo nayo), kubvunza-ne-komiti (sarudza paisingabvumirani nemubatanidzwa), uye sampling yekusiyana (inovhara nzvimbo dzakasiyana dzedata). Wakaitwa nemazvo, kudzidza kunoshanda kunogona kuenderana nezere-dataset kurongeka uchishandisa mashoma mabhii, ndosaka ichizivikanwa mukufungidzira kwekurapa, NLP, uye chero dura apo nyanzvi yekuzivisa inononoka kana kudhura.
Kuitwa muNyika yeChokwadi
Chikwata cheradiology chinodzidzisa chidhina chebundu nekuita kuti mureza wemodhiyo utarise zvakanyanya kujeka kuti nyanzvi dzeradiology dzinyore, dzichicheka maawa ezvirevo zvinoshamisa.
Iyo spam kana yemukati-moderation sisitimu inotarisana nemuganho mameseji ayo asina chokwadi nezvayo kune vanhu vanoongorora, inovandudza nekukurumidza pamakesi akaomarara.
Kambani inoona nezvekutaura inosarudza zvikamu zvekuteerera apo modhi yayo isinganyatso zivikanwi (mataurirwo, ruzha) kutumira kuti inyorerwe, pane kunyora zvidimbu zvisina tsarukano.
Iyo e-commerce katalog inoshandisa query-by-committee kutora mifananidzo yechigadzirwa apo vazhinji veclassifiers vasingabvumirani, vachiisa pamberi payo yekunyora chikamu chemaoko.
Ramangwana ReKudzidza Nekushingaira
Kudzidzira kunoshanda kuri kuwedzera kupetwa nemhando dzakakura dzakadzidziswa uye dzenheyo, uko chinangwa chinochinja kubva pakunyora zvese kuenda kukugadzirisa zvakachipa pamienzaniso mishoma yeukoshi hwepamusoro. Tarisira kubatanidzwa kwakasimba nekutarisisa kusina simba, kuzvitarisira-yega pretraining, uye munhu-mu-the-loop tooling iyo inopa mavara evanoongorora kuti vasimbise pane kugadzira. Sezvo mutengo wekunyora uchitonga akawanda chaiwo ekutumirwa, otomatiki kusarudzwa pamwe neanoshanda annotation interfaces icharamba iri pakati pemamodheru ekuvaka mune akasarudzika, anoshomeka dhata semushonga uye mutemo.
Kuzvishandisa
Nei zvichikosha
- Sarudzo dzakajeka. Inokubatsira kuparadzanisa zvakajeka zvichemo zvehunyanzvi kubva mumutauro wekushambadzira.
- Mutengo uye bhajeti. Iwe unogona kubvunza zvirinani kuita mibvunzo usati washandisa mari kana nguva.
- Team uye workflow. Zvikwata zvine nzwisiso yakagovaniswa inoita zvirinani chigadzirwa, mutemo, uye sarudzo dzekudzidza.
Chenjera
- Zvikwata zvakasiyana zvinogona kushandisa izwi rimwechete zvakasiyana, saka tsanangura nzvimbo nekukurumidza.
- Benchmarks inogona kutaridzika yakasimba nepo chaiyo-yenyika kuita isina kuenzana.
- Kuregeredza mhando yedata uye zvirongwa zvekuongorora zvinowanzogadzira mhedzisiro isina kusimba.
Matanho anotevera
- Tanga netsanangudzo yemutauro wakajeka yemhedzisiro yaunoda.
- Sarudza metric imwe yekubudirira uye imwe yekutadza mamiriro usati waedzwa.
- Mhanya mutyairi mudiki ane data remumiriri, kwete demo rakakwenenzverwa.
- Nyora apo Active Learning inobatsira uye uko nzira dzakareruka dziri nani.
Ramba Uchiongorora
Tsamba yemahara
Tora iyo yemazuva ese AI pfupiso
Nhatu dzakasimbiswa dzeAI nyaya mangwanani ega ega pakati pevhiki, dzakanyorwa neChirungu chakajeka. Yemahara zvachose, hapana ads.
Imwe email zuva rega rega revhiki. Bvisa kunyoresa kamwe chete. Hatimbotengese kana kugovera kero yako.
Zviedze iwe pachako
Take the Active Learning quiz
Mhinduro yepakarepo pamhinduro yega yega, uye chitupa chinogovaniswa chine ID inobatika kana wapedza kosi.
Tsigira dzidzo yemahara yeAI. AI Understanding ndeye 501(c)(3) isingabatsiri - hapana ads, hapana paywall, zvachose. Ita chipo
Mibvunzo inowanzo bvunzwa
What is Active Learning?
Kudzidzira kunoshanda inzira yekudzidzisa apo modhi pachayo inosarudza kuti ndeipi mienzaniso isina kunyorwa iyo munhu anofanira kunyora inotevera. Izvo zvine basa nekuti kunyora data kunodhura, uye sarudzo yakangwara inogona kusvika pakurongeka kwepamusoro nechidimbu chezvirevo.
A team has a large unlabeled pool but limited expert time. What does active learning ask the team to label next?
The guide describes querying an oracle for selected informative examples, then using their labels in training.
In the guide’s active-learning loop, what is the oracle?
The oracle provides labels for selected examples; it is commonly a human annotator or expert.
Two product classifiers disagree strongly on a category. Which strategy would prioritize that example for labeling?
Query-by-committee selects examples on which several models disagree.
A classifier assigns two classes almost equal probabilities for an unlabeled item. Why might uncertainty sampling query it?
A small margin between leading class scores is one uncertainty signal described by the guide.
Why might a batch selector combine diversity with uncertainty?
The guide warns that greedily chasing uncertainty can ignore other parts of the data.
Ramba uchidzidza
Mazano anoenderana
Mamwe madhairekitori akasarudzirwa nyaya iyi