{"id":116353,"date":"2026-01-02T17:40:08","date_gmt":"2026-01-02T17:40:08","guid":{"rendered":"https:\/\/ekamu.net\/?p=116353"},"modified":"2026-01-02T17:40:08","modified_gmt":"2026-01-02T17:40:08","slug":"deepseek-yeni-modeliyle-yapay-zekalarin-egitim-maliyetini-daha-da-asagi-cekebilir","status":"publish","type":"post","link":"https:\/\/ekamu.net\/index.php\/2026\/01\/02\/deepseek-yeni-modeliyle-yapay-zekalarin-egitim-maliyetini-daha-da-asagi-cekebilir\/","title":{"rendered":"DeepSeek, yeni modeliyle yapay zekalar\u0131n e\u011fitim maliyetini daha da a\u015fa\u011f\u0131 \u00e7ekebilir"},"content":{"rendered":"

<\/span> Ge\u00e7ti\u011fimiz y\u0131l\u0131n ba\u015f\u0131nda \u00e7\u0131kard\u0131\u011f\u0131 DeepSeek-R1 ile yapay zeka d\u00fcnyas\u0131nda dengeleri de\u011fi\u015ftiren DeepSeek<\/strong>, \u00e7ok yak\u0131nda yine deprem etkisi yaratacak bir modelle kar\u015f\u0131m\u0131za \u00e7\u0131kabilir. \u00c7in merkezli \u015firketin yapay zeka alan\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7acak yeni bir model \u00fczerinde \u00e7al\u0131\u015ft\u0131\u011f\u0131 bir s\u00fcredir konu\u015fuluyordu. Bu hafta DeepSeek taraf\u0131ndan yay\u0131mlanan bir makale, bu yeni modelde bizi ne gibi yeniliklerin bekledi\u011fini daha a\u00e7\u0131k \u015fekilde ortaya koydu. G\u00f6r\u00fcnen o ki R1 gibi DeepSeek’in yeni modeli de yapay zekalar\u0131n e\u011fitilmesi konusunda sekt\u00f6re yeni kap\u0131lar aralayacak.<\/strong> <\/figure>\n

DeepSeek taraf\u0131ndan payla\u015f\u0131lan makalede, \u201cManifold-Constrained Hyper-Connections\u201d (mHC) ad\u0131 verilen yeni bir derin \u00f6\u011frenme mimarisi<\/strong> tan\u0131t\u0131l\u0131yor. DeepSeek’in kurucusu olan Liang Wenfeng<\/strong>\u2019in yan\u0131 s\u0131ra Zhenda Xie, Yixuan Wei ve Huanqi Cao’nun da imzas\u0131n\u0131 ta\u015f\u0131yan \u00e7al\u0131\u015fma, b\u00fcy\u00fck sinir a\u011flar\u0131nda (neural network) e\u011fitim s\u0131ras\u0131nda ortaya \u00e7\u0131kan karars\u0131zl\u0131k ve \u00f6l\u00e7eklenme problemlerini azaltmay\u0131 hedefliyor. Ara\u015ft\u0131rmac\u0131lara g\u00f6re mHC, mevcut yakla\u015f\u0131mlara k\u0131yasla hem daha tutarl\u0131 bir e\u011fitim s\u00fcreci sunuyor hem de ciddi ek hesaplama maliyetleri olu\u015fturmadan daha b\u00fcy\u00fck modellere \u00f6l\u00e7eklenebiliyor. Bu da, b\u00fcy\u00fck dil modellerinin e\u011fitim maliyetlerini d\u00fc\u015f\u00fcrmeye y\u00f6nelik \u00f6nemli bir ad\u0131m olarak g\u00f6r\u00fcl\u00fcyor<\/strong>. Hat\u0131rlarsan\u0131z DeepSeek-R1’in en \u00e7ok ses getirdi\u011fi nokta da bu konudaki ba\u015far\u0131s\u0131yd\u0131.<\/p>\n

DeepSeek\u2019in geli\u015ftirdi\u011fi bu mimari, temellerini ByteDance<\/strong> ara\u015ft\u0131rmac\u0131lar\u0131n\u0131n 2024 y\u0131l\u0131nda tan\u0131tt\u0131\u011f\u0131 \u201chyper-connections\u201d (hiper ba\u011flant\u0131lar) yakla\u015f\u0131m\u0131ndan al\u0131yor. Bu yakla\u015f\u0131m, g\u00fcn\u00fcm\u00fczde pek \u00e7ok b\u00fcy\u00fck dil modelinin temelini olu\u015fturan ResNet<\/strong> mimarisinde\u00a0bilginin katmanlar aras\u0131nda do\u011frudan aktar\u0131lmas\u0131na imk\u00e2n tan\u0131yan yap\u0131y\u0131 geni\u015fleterek, sinyallerin a\u011f i\u00e7inde daha tutarl\u0131 bi\u00e7imde ilerlemesini sa\u011flamay\u0131 ama\u00e7l\u0131yordu. Ancak ByteDance\u2019in \u00f6nerdi\u011fi yap\u0131, \u00f6zellikle \u00e7ok b\u00fcy\u00fck modellerde ciddi bellek y\u00fck\u00fc olu\u015fturmas\u0131 nedeniyle pratikte \u00f6l\u00e7eklenme sorunlar\u0131 yarat\u0131yordu.<\/strong> DeepSeek\u2019in \u00e7al\u0131\u015fmas\u0131, bu noktada devreye girerek s\u00f6z konusu yap\u0131y\u0131 daha uygulanabilir h\u00e2le getiriyor.<\/p>\n

DeepSeek Yeni Modelini Bu Mimariyle Geli\u015ftiriyor<\/b><\/p>\n

mHC mimarisinin en \u00f6nemli fark\u0131, katmanlar aras\u0131 do\u011frudan bilgi ak\u0131\u015f\u0131n\u0131 rastgele geni\u015fletmek yerine, bu ak\u0131\u015f\u0131 belirli matematiksel kurallar \u00e7er\u00e7evesinde tan\u0131mlanm\u0131\u015f bir uzay (manifold) i\u00e7inde tutmas\u0131. Bu sayede \u201cidentity mapping\u201d olarak adland\u0131r\u0131lan ve sinir a\u011flar\u0131nda sinyallerin y\u00fczlerce katman boyunca bozulmadan iletilmesini sa\u011flayan kritik \u00f6zellik yeniden kazan\u0131l\u0131yor. Ara\u015ft\u0131rmac\u0131lar, bu k\u0131s\u0131t sayesinde sinyallerin ne kayboldu\u011funu ne de kontrolden \u00e7\u0131karak patlad\u0131\u011f\u0131n\u0131; dolay\u0131s\u0131yla e\u011fitim s\u00fcrecinin \u00e7ok daha stabil h\u00e2le geldi\u011fini belirtiyor. mHC mimarisi; 3 milyar, 9 milyar ve 27 milyar parametreli modeller \u00fczerinde denendi<\/strong> ve ciddi bir ek hesaplama y\u00fck\u00fc olu\u015fturmadan sorunsuz \u015fekilde \u00f6l\u00e7eklenebildi\u011fi g\u00f6sterildi.<\/p>\n

DeepSeek taraf\u0131ndan payla\u015f\u0131lan bu t\u00fcr teknik makaleler, yakla\u015fan yeni modelin habercisi<\/strong> olarak g\u00f6r\u00fcl\u00fcyor. Liang Wenfeng, daha \u00f6nceki modelleri yay\u0131nlamadan \u00f6nce de benzer makaleler payla\u015fm\u0131\u015ft\u0131. Bu y\u00fczden yeni modelin de bu mimari \u00fczerine kurulu olarak gelece\u011fi d\u00fc\u015f\u00fcn\u00fcl\u00fcyor. Beklentileri epey y\u00fckselten bu modelin tam olarak ne zaman tan\u0131t\u0131laca\u011f\u0131 hen\u00fcz kesinle\u015fmi\u015f de\u011fil. Ancak 17 \u015eubat’tan \u00f6nce tan\u0131t\u0131lm\u0131\u015f olaca\u011f\u0131 d\u00fc\u015f\u00fcn\u00fcl\u00fcyor.<\/strong><\/p>\n\n

<\/span><\/p>\n

Kaynak :\u00a0https:\/\/www.donanimhaber.com\/deepseek-yeni-modeliyle-dengeleri-bir-kez-daha-degistirebilir–200405<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Ge\u00e7ti\u011fimiz y\u0131l\u0131n ba\u015f\u0131nda \u00e7\u0131kard\u0131\u011f\u0131 DeepSeek-R1 ile yapay zeka d\u00fcnyas\u0131nda dengeleri de\u011fi\u015ftiren DeepSeek, \u00e7ok yak\u0131nda yine deprem etkisi yaratacak bir modelle kar\u015f\u0131m\u0131za \u00e7\u0131kabilir. \u00c7in merkezli \u015firketin yapay zeka alan\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7acak yeni bir …<\/p>\n","protected":false},"author":1,"featured_media":116354,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8],"tags":[6384,1189,452,4312],"class_list":["post-116353","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-teknoloji","tag-deepseek","tag-mimari","tag-model","tag-modelle"],"_links":{"self":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/116353","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/comments?post=116353"}],"version-history":[{"count":1,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/116353\/revisions"}],"predecessor-version":[{"id":116356,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/posts\/116353\/revisions\/116356"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/media\/116354"}],"wp:attachment":[{"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/media?parent=116353"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/categories?post=116353"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ekamu.net\/index.php\/wp-json\/wp\/v2\/tags?post=116353"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}