Raibhurari yemahara yeAI

Dzidza AI.Kusununguka nekusingaperi.

1019 magwara eChirungu, nzira dzakarongeka dzekudzidza, uye raibhurari yakavhurika — yakavakwa neyakazvimiririra 501(c)(3) isingabatsiri kuitira kuti chero munhu anzwisise AI yemazuva ano.

1019Mazano emahara
9Musoro wenyaya
~2 minPer gwara
~34hNguva yekuverenga

Tanga pano

Vashanu Mhedzisiro-yakavakirwa makosi

Kosi yega yega inosanganisira mhedzisiro yakajeka, hunyanzvi hwemepu, zviitiko zvekudzidzira, uye yakashandiswa capstone.

Musoro wenyaya

Bhurawuza nekumhanya

Svetukira munharaunda yaunofarira. Nzira yega yega ine akawanda akajeka-Chirungu magwaro.

Raibhurari yakazara

Zvese zvinotungamira

1019 ye 1019 Guides showed. Filter by track or search above.

Tekinoroji

Consistency Regularization muSemi-Yakatariswa Kudzidza

Consistency regularization inodzidzisa modhi kuti ipe mhinduro imwechete kana mapindiro asina kunyorwa akavhiringika munzira diki, dzinochengetedza mavara.

2 VerengaVerenga
Tekinoroji

Yakaoma Parameter Kugovera muMulti-Task Networks

Yakaoma paramende kugovera ndiyo yakasarudzika-yakawanda-basa yekudzidza dhizaini apo akati wandei mabasa anogovera akafanana akavanzika akaturikidzana uye anongopatsanurwa kuita akasiyana anobuda 'misoro'…

2 VerengaVerenga
Tekinoroji

Gating uye Routing muConditional Computation

Gating uye nzira rega neural network ishande chete zvikamu zvainoda kune yega yega yekuisa panzvimbo yekumhanyisa modhi yese nguva dzese.

2 VerengaVerenga
Tekinoroji

Gumbel-Softmax uye Reparameterization

Gumbel-Softmax idhiri rinoita kuti neural network 'sample' kubva kune discrete mapoka ichiri kudzidziswa ne gradient descent.

2 VerengaVerenga
Tekinoroji

Yakarurama-Kuburikidza Estimator

Iyo Yakatwasuka-Kuburikidza Estimator (STE) iri nyore trick yekudzidzisa network ine yakaoma, isingasiyanise matanho senge kutenderedza kana chikumbaridzo.

2 VerengaVerenga
Basics

Group Normalization

Boka Normalization inzira inodzikamisa neural network kudzidziswa nekujairisa maficha mukati memapoka madiki echiteshi, yakazvimiririra kune yega yega…

2 VerengaVerenga
Basics

Gated Recurrent Units

A Gated Recurrent Unit (GRU) imhando yakagadziridzwa yeinodzokororwa neural network cell iyo inoshandisa magedhi maviri kusarudza ruzivo rwekuchengeta uye chekukanganwa…

2 VerengaVerenga
Tekinoroji

Bidirectional Recurrent Networks

A bidirectional recurrent network inoverenga nhevedzano kumberi nekumashure, saka chinomiririra chinzvimbo chega chega chinokwevera pane zvakapfuura uye nezveramangwana.

2 VerengaVerenga
Tekinoroji

Attention Rollout uye Head Pruning

Attention rollout inzira yekutsvaga kuti ruzivo rwunofamba sei kuburikidza neTransformer's yakaturikidzana yekutarisisa maseru kutsanangura kuti ndeapi ma tokens ekuisa anokanganisa...

2 VerengaVerenga
Mutauro AI

Contrastive Decoding

Contrast decoding inoburitsa mavara emhando yepamusoro nekubvisa maitiro emutauro mudiki, usina simba kubva kune iwo mukuru, wakasimba.

2 VerengaVerenga
Mutauro AI

Inotungamirirwa Beam Search ine Constraints

Kutsvaga kwebeam kunomanikidza kubuda kwemodhi yemutauro kugutsa zvinodiwa zvakaoma, sekubatanidza mazwi chaiwo kana kuenzanisa girama, uchiri...

2 VerengaVerenga
Mutauro AI

Minimum Bayes Risk Decoding

Minimum Bayes Risk (MBR) decoding inotora inobuda iyo inonyanya kufanana nezvimwe zvakawanda zvingangobuda, pane imwechete yepamusoro-inogoneka imwe.

2 VerengaVerenga

Wapedza kuverenga? Ndiratidze.

Check what you learned with a topic quiz, then explore our structured courses or work toward a certificate. Every guide stays free to read.