Netwọk okporo ụzọ wee gaa njikọ
Mwụpụ njikọ na-eme ka ozi wụba n'ígwé gafee, na netwọk okporo ụzọ bụ ụdị echiche a gbachiri agbachi.
Nchịkọta
They solve the problem of training very deep networks, which paved the way for ResNets and modern deep learning.
Ime miri emi
Tupu ịmalite njikọ, ikpokọta ọtụtụ ọkwa mere ka netwọk sie ike karị, ọ kaghị mma, ịzụ ọzụzụ n'ihi na gradients na-apụ n'anya ma na-eweda akara ngosi. Netwọk okporo ụzọ, ewebata na 2015, gbakwụnyere ọnụ ụzọ mmụta mmụta nke na-achịkwa ole ntinye oyi akwa ka agbanwere ka ebufe ya ozugbo, nke sitere na gating LSTM. N'oge na-adịghị anya ka nke ahụ gasịrị, ResNets mere ka nke a dị mfe na njikọ fọdụrụnụ, ebe oyi akwa na-amụta ọrụ fọdụrụnụ na ntinye ya na ntinye ya site na ụzọ mkpirisi njirimara. Ụzọ mkpirisi ndị a na-emepụta ụzọ kpọmkwem maka gradients ka ọ na-aga azụ, na-eme ka o kwe omume ịzụ netwọk ọtụtụ narị narị ma ọ bụ ọbụna otu puku akwa dị omimi. Njikọ mwụpụ na-apụta ugbu a n'ebe niile, gụnyere U-Nets, DenseNets, na transformers.
Nghọta nka nka
Ihe mgbochi ihe fọdụrụ na-agbakọ mmepụta = F(x) + x, yabụ na netwọk kwesịrị ịmụta F(x) fọdụrụnụ karịa maapụ zuru ezu. N'oge mgbasa ozi, okwu njirimara mgbakwunye na-agafe gradients na-agbanweghị, na-apụ apụ na gradients na-apụ n'anya. Netwọk okporo ụzọ na-ejikọta nke a site na iji ọnụ ụzọ mgbanwe T wee buru ọnụ ụzọ, mmepụta = F(x)*T(x) + x*(1 - T(x)), ebe a na-amụta T ma dị n'etiti 0 na 1.
Mmetụta atụmatụ
Ọnụ ego na mmefu ego
Mkpebi ihe owuwu ụlọ na-akwalite arụmọrụ yana ọnụ ahịa ọrụ ruo ọtụtụ afọ.
Mkpebi doro anya
Nkà mmụta nka na-enyere ndị otu egwuregwu aka ịhọrọ nchịkọta ziri ezi, ọ bụghị naanị nke kachasị ọhụrụ.
Quality akara
Nhọrọ injinia ka mma na-ebelata ihe omume ntụkwasị obi na mmepụta.
Ọdịnihu nke netwọkụ okporo ụzọ na ịgafe njikọ
Njikọ mwụpụ ugbu a bụ ngọngọ ụlọ na-adịghị mma karịa aghụghọ nhọrọ. Onye ngbanwe ọ bụla na-eji njikọ fọdụrụ gburugburu nlebara anya ya na ndị na-ebugharị ya, ma ha ka dị mkpa na ụdị mgbasa ozi, nkebi U-Nets, na netwọkụ eserese. Nchọcha na-enyocha ndobe nzizi ka mma, nlegharị anya nke ụzọ ndị fọdụrụ ka a ga-amụta na ya, yana ihe nrụgharịgharị nke na-emegharị ọrụ iji chekwaa ebe nchekwa. Echiche bụ isi nke ichekwa mgbaàmà n'ofe omimi ga-adịgide ka ụdị na-etolite.
Mmejuputa n'ezie n'ụwa
ResNet-50 na ResNet-152 na-eji ụzọ mkpirisi fọdụrụnụ na-azụ ndị nhazi ihe onyonyo dị omimi.
Ndị ntụgharị na ụdị asụsụ buru ibu na-ekekọta njikọ ndị fọdụrụ na nlebara anya na n'ígwé ndị na-ebugharị
Njikọ U-Net skip na-agafe nkọwa zuru oke site na koodu ntinye gaa na decoder maka nkewa onyonyo ahụike.
DenseNet na-ejikọ oyi akwa ọ bụla na ọkwa ndị ọzọ na-esote, na-agba ume ka ejiri ya mee ihe ma na-eme ka usoro gradient dị mfe.
Ihe ize ndụ & okporo ụzọ nche
Ịkwalite otu akara ngosi nwere ike zoo adịghị ike sistemụ sara mbara.
A na-eledakarị ihe akụrụngwa na ụgwọ ọrụ anya.
Ọdịiche nchekwa na nleba anya nwere ike itolite ka sistemu na-adịwanye mgbagwoju anya.
Map mmejuputa
Kọwaa latency, ịdịmma na ebumnuche ọnụ ahịa tupu mmejuputa ya.
Benchmark n'okpuru ibu dị adị na ọnọdụ data.
Nleba anya akụrụngwa maka mperi, ịkpafu na mmetụta onye ọrụ.
Kwadebe ụzọ nzaghachi azụghachi azụ na ihe omume tupu ịchachaa.
Nọgide na-eme nchọpụta
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Ntuziaka na-esote
Netwọk Siamese na mfu Triplet
Ajụjụ a na-ajụkarị
What is Highway Networks and Skip Connections?
Mwụpụ njikọ na-eme ka ozi wụba n'ígwé gafee, na netwọk okporo ụzọ bụ ụdị echiche a gbachiri agbachi. Ha na-edozi nsogbu nke ịzụ netwọk dị omimi, nke meghere ụzọ maka ResNets na mmụta miri emi nke ọgbara ọhụrụ.
Kedu isi nsogbu ka njikọ mwụpụ na-enyere aka dozie na netwọk dị omimi?
Site n'inye ụzọ ziri ezi, ịwụpụ njikọ na-eme ka gradients na akara na-agafe na ngwugwu miri emi, na-eme ka netwọk miri emi nwee ike ịzụ ya.
Kedu ihe ihe mgbochi ihe fọdụrụ na-agbakọ?
Mgbochi fọdụrụnụ na-agbakwụnye ntinye x na mmepụta F(x) gbanwere, ya mere oyi akwa na-amụta naanị ihe fọdụrụnụ.
Kedu ihe kpaliri usoro ntinye ọnụ na netwọk okporo ụzọ?
Netwọk okporo ụzọ gbaziri echiche nke ọnụ ụzọ amụtara site na LSTM iji chịkwaa ole ozi agbanwere ka ọ na-ebufe ya.
Na netwọk okporo ụzọ, kedu ihe ngbanwe ọnụ ụzọ T(x) na-achịkwa?
Nsonaazụ bụ F (x) * T (x) + x* (1 - T (x)), yabụ T na-ekpebi nguzozi n'etiti ịtụgharị na iburu ntinye.
Kedu ihe owuwu ọgbara ọhụrụ na-adabere na njikọ ndị fọdụrụ n'akụkụ ndị sublayers ya?
Ndị na-agbanwe agbanwe na-edobe njikọ ndị fọdụrụ na gburugburu ma nlebara anya ha na ndị na-ebubata ihe n'ihu dị ka ụkpụrụ ọkọlọtọ.