{"id":11925,"date":"2026-05-15T04:44:31","date_gmt":"2026-05-15T04:44:31","guid":{"rendered":"https:\/\/virconlegal.com\/term\/transformer-mimarisi\/"},"modified":"2026-05-15T05:10:32","modified_gmt":"2026-05-15T05:10:32","slug":"transformer-mimarisi","status":"publish","type":"term","link":"https:\/\/virconlegal.com\/tr\/term\/transformer-mimarisi\/","title":{"rendered":"Transformer Mimarisi"},"content":{"rendered":"

TLDR:<\/h3>\n

Transformer, neredeyse t\u00fcm modern LLM’lerin<\/a> (GPT, Claude, Gemini, Llama), g\u00f6r\u00fcnt\u00fc \u00fcretim modellerinin, kod modellerinin ve di\u011fer temel modellerin altta yatan sinir a\u011f\u0131 mimarisidir. 2017’de Google ara\u015ft\u0131rmac\u0131lar\u0131 taraf\u0131ndan “Attention Is All You Need” makalesinde tan\u0131t\u0131lan Transformer’\u0131n \u00f6z-dikkat (self-attention) mekanizmas\u0131, bug\u00fcn\u00fcn \u00fcretken yapay zeka<\/a> devrimini \u00fcreten \u00f6l\u00e7eklendirmeyi m\u00fcmk\u00fcn k\u0131ld\u0131.<\/p>\n

Dikkat (Attention) Mekanizmas\u0131<\/h3>\n

Transformer’lar dizileri (metin, g\u00f6r\u00fcnt\u00fc, ses) “dikkat” (attention) hesaplayarak i\u015fler\u2014girdideki her token ile di\u011fer her token aras\u0131ndaki a\u011f\u0131rl\u0131kl\u0131 ili\u015fki. \u00d6z-dikkat (self-attention), modelin girdinin hangi b\u00f6l\u00fcmlerinin her \u00e7\u0131kt\u0131 token’\u0131 i\u00e7in ilgili oldu\u011funu dinamik olarak belirlemesini sa\u011flar. S\u0131ral\u0131 verileri kat\u0131 bir \u015fekilde soldan sa\u011fa i\u015fleyen tekrarlayan sinir a\u011flar\u0131ndan (RNN\/LSTM) farkl\u0131 olarak, Transformer’lar t\u00fcm pozisyonlar\u0131 paralel olarak i\u015fler, GPU’larda e\u011fitim verimlili\u011fini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r ve modern modellerin b\u00fcy\u00fck \u00f6l\u00e7e\u011fini m\u00fcmk\u00fcn k\u0131lar.<\/p>\n

Transformer’lar Neden Kazand\u0131<\/h3>\n

Transformer’lar\u0131 bask\u0131n mimari yapan \u00e7e\u015fitli fakt\u00f6rler vard\u0131r: paralelle\u015ftirilebilir e\u011fitim (tekrarlayan ba\u011f\u0131ml\u0131l\u0131klar yok), g\u00fc\u00e7l\u00fc \u00f6l\u00e7eklendirme \u00f6zellikleri (performans daha fazla parametre, daha fazla veri, daha fazla hesaplama ile g\u00fcvenilir \u015fekilde iyile\u015fir), esneklik (ayn\u0131 mimari k\u00fc\u00e7\u00fck de\u011fi\u015fikliklerle metin, g\u00f6r\u00fcnt\u00fc, ses ve kodu ele al\u0131r) ve \u00f6l\u00e7ekte yeteneklerin ortaya \u00e7\u0131kmas\u0131 (ba\u011flam i\u00e7i \u00f6\u011frenme, d\u00fc\u015f\u00fcnce zinciri muhakemesi, talimat takip).<\/p>\n

Varyantlar ve Modern Geli\u015fmeler<\/h3>\n

Orijinal kodlay\u0131c\u0131-kod \u00e7\u00f6z\u00fcc\u00fc Transformer bir\u00e7ok varyant do\u011furmu\u015ftur: yaln\u0131zca kodlay\u0131c\u0131 modelleri (BERT, s\u0131n\u0131fland\u0131rma ve embedding<\/a> i\u00e7in kullan\u0131l\u0131r), yaln\u0131zca kod \u00e7\u00f6z\u00fcc\u00fc modelleri (GPT ailesi, \u00fcretim i\u00e7in kullan\u0131l\u0131r), kodlay\u0131c\u0131-kod \u00e7\u00f6z\u00fcc\u00fc (T5, \u00e7eviri ve \u00f6zetleme i\u00e7in kullan\u0131l\u0131r), g\u00f6r\u00fcnt\u00fc Transformer’lar\u0131 (ViT, g\u00f6r\u00fcnt\u00fc anlama i\u00e7in) ve her ileri ge\u00e7i\u015fte yaln\u0131zca parametrelerin bir alt k\u00fcmesini etkinle\u015ftiren Uzman Kar\u0131\u015f\u0131m\u0131 (MoE) varyantlar\u0131 (Mixtral, GPT-4 mimarisi). Son geli\u015fmeler aras\u0131nda durum-uzay modelleri (Mamba) ve \u00e7ok uzun ba\u011flamlarda Transformer s\u0131n\u0131rlamalar\u0131n\u0131 a\u015fmay\u0131 ama\u00e7layan hibrit mimariler yer al\u0131r.<\/p>\n","protected":false},"excerpt":{"rendered":"

TLDR: Transformer, neredeyse t\u00fcm modern LLM’lerin (GPT, Claude, Gemini, Llama), g\u00f6r\u00fcnt\u00fc \u00fcretim modellerinin, kod modellerinin ve di\u011fer temel modellerin altta yatan sinir a\u011f\u0131 mimarisidir. 2017’de Google ara\u015ft\u0131rmac\u0131lar\u0131 taraf\u0131ndan “Attention Is All You Need” makalesinde tan\u0131t\u0131lan Transformer’\u0131n \u00f6z-dikkat (self-attention) mekanizmas\u0131, bug\u00fcn\u00fcn \u00fcretken yapay zeka devrimini \u00fcreten \u00f6l\u00e7eklendirmeyi m\u00fcmk\u00fcn k\u0131ld\u0131. Dikkat (Attention) Mekanizmas\u0131 Transformer’lar dizileri (metin, g\u00f6r\u00fcnt\u00fc, […]<\/p>\n","protected":false},"author":4,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"footnotes":""},"categories":[],"class_list":["post-11925","term","type-term","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11925","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term"}],"about":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/types\/term"}],"author":[{"embeddable":true,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/comments?post=11925"}],"version-history":[{"count":1,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11925\/revisions"}],"predecessor-version":[{"id":12314,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/term\/11925\/revisions\/12314"}],"wp:attachment":[{"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/media?parent=11925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/virconlegal.com\/tr\/wp-json\/wp\/v2\/categories?post=11925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}