{"id":445,"date":"2025-12-04T20:00:22","date_gmt":"2025-12-04T17:00:22","guid":{"rendered":"https:\/\/neku.ai\/self-attention-yapay-zeka\/"},"modified":"2025-12-04T20:00:47","modified_gmt":"2025-12-04T17:00:47","slug":"self-attention-yapay-zeka","status":"publish","type":"post","link":"https:\/\/neku.ai\/en\/self-attention-yapay-zeka\/","title":{"rendered":"Self Attention ile Yapay Zeka Modellerinde Ba\u011flam Anlamland\u0131rma"},"content":{"rendered":"

Self attention nedir<\/strong><\/h1>\n

Giri\u015f<\/strong><\/h3>\n

Self attention, yapay zekada \u00f6zellikle b\u00fcy\u00fck dil modelleri (LLM) mimarilerinin temel bile\u015fenlerinden biridir. Bir modelin girdideki her \u00f6\u011fe aras\u0131nda ili\u015fkileri anlamas\u0131n\u0131 sa\u011flar ve do\u011fru ba\u011flam kurarak daha tutarl\u0131 \u00e7\u0131kt\u0131lar \u00fcretir. Bu kavram, Temel AI serisinde hem geli\u015ftirici hem de \u00fcr\u00fcn ekiplerinin modern algoritma davran\u0131\u015flar\u0131n\u0131 kavramas\u0131 i\u00e7in kritik \u00f6neme sahiptir.<\/p>\n

\n

Self attention nedir tan\u0131m\u0131<\/strong><\/h3>\n

Self attention, bir veri dizisinin i\u00e7indeki her \u00f6\u011fenin, di\u011fer \u00f6\u011felerle olan ili\u015fkisini dinamik olarak hesaplamas\u0131na izin veren dikkat mekanizmas\u0131d\u0131r. Model, hangi kelimenin veya verinin belirli bir noktada daha fazla a\u011f\u0131rl\u0131k ta\u015f\u0131d\u0131\u011f\u0131n\u0131 tahmin eder. B\u00f6ylece girdinin t\u00fcm b\u00f6l\u00fcmleri birbirini de\u011ferlendirir, sadece s\u0131radaki \u00f6\u011feye de\u011fil t\u00fcm ba\u011flama odaklan\u0131r.<\/p>\n

\n

self attention nas\u0131l \u00e7al\u0131\u015f\u0131r<\/strong><\/h3>\n

Self attention, \u00f6zellikle Transformer mimarilerinde Query, Key ve Value vekt\u00f6rleri \u00fczerinden matematiksel a\u011f\u0131rl\u0131k hesaplamalar\u0131 ile \u00e7al\u0131\u015f\u0131r. Her \u00f6\u011fe kendine ait bir Query \u00fcretir; bu Query di\u011fer \u00f6\u011felerin Key de\u011ferleriyle etkile\u015fime girerek benzerlik puanlar\u0131n\u0131 olu\u015fturur. Bu puanlar, Value vekt\u00f6rleriyle \u00e7arp\u0131larak modelin ilgili \u00e7\u0131kt\u0131s\u0131n\u0131 belirler.<\/p>\n

Temel parametreler ve ayarlar<\/strong><\/h4>\n

Bu s\u00fcre\u00e7te temel parametreler genellikle boyut (dimension), ba\u015fl\u0131k say\u0131s\u0131 (attention heads) ve \u00f6l\u00e7ekleme fakt\u00f6r\u00fcd\u00fcr. Do\u011fru boyutland\u0131rma, modelin hem e\u011fitim h\u0131z\u0131n\u0131 hem de \u00f6\u011frenme kapasitesini do\u011frudan etkiler. \u00d6zellikle \u00e7ok ba\u015fl\u0131kl\u0131 self attention, farkl\u0131 semantik ili\u015fkileri paralel olarak yakalayarak modelin anlam derinli\u011fini art\u0131r\u0131r.<\/p>\n

S\u0131k yap\u0131lan hatalar ve ka\u00e7\u0131nma y\u00f6ntemleri<\/strong><\/h4>\n

Yayg\u0131n hatalardan biri, Query-Key matrislerinin uygun \u015fekilde normalize edilmemesidir. Bu, modelin belirli terimlere a\u015f\u0131r\u0131 odaklanmas\u0131na yol a\u00e7ar. Ka\u00e7\u0131nmak i\u00e7in \u00f6l\u00e7ekleme fakt\u00f6r\u00fcn\u00fcn standartla\u015ft\u0131r\u0131lmas\u0131 ve dikkat skorlar\u0131n\u0131n softmax i\u015flemiyle dengelenmesi gerekir. Ayr\u0131ca, yanl\u0131\u015f boyutland\u0131r\u0131lm\u0131\u015f embeddings performans kayb\u0131na neden olabilir.<\/p>\n

Ger\u00e7ek sistemlerde uygulama \u00f6rnekleri<\/strong><\/h4>\n

LLM tabanl\u0131 bir m\u00fc\u015fteri hizmeti botunda self attention, kullan\u0131c\u0131n\u0131n bir c\u00fcmlede sordu\u011fu as\u0131l niyeti belirlemeye yard\u0131mc\u0131 olur. \u00d6rne\u011fin, ayn\u0131 mesajda ge\u00e7en \u201cfatura\u201d ve \u201ciptal\u201d kelimeleri aras\u0131nda ba\u011f kurarak do\u011fru yan\u0131t\u0131 se\u00e7er. Bu yakla\u015f\u0131m, sistemin daha do\u011fal ve h\u0131zl\u0131 karar vermesini sa\u011flar.<\/p>\n

\n

Teknik a\u00e7\u0131klama (derin seviye)<\/strong><\/h3>\n

Ba\u015flang\u0131\u00e7 seviyesinde self attention, \u201cher kelimenin di\u011fer kelimelere dikkat etti\u011fi\u201d bir mekanizma olarak d\u00fc\u015f\u00fcn\u00fclebilir. Orta seviyede, bu i\u015flem matris \u00e7arp\u0131mlar\u0131 (Q x K\u1d57) ve ard\u0131ndan softmax ile normalizasyon i\u00e7erir. \u0130leri d\u00fczeyde, Transformer bloklar\u0131 i\u00e7inde bu s\u00fcre\u00e7 paralel ba\u015fl\u0131klar halinde \u00e7al\u0131\u015f\u0131r ve \u00e7\u0131kt\u0131lar birle\u015ftirilip lineer d\u00f6n\u00fc\u015f\u00fcm uygulan\u0131r. B\u00f6ylece model, hem global hem lokal ba\u011flam\u0131 senkronize \u015fekilde de\u011ferlendirir.<\/p>\n

\n

\u0130\u015fletmeler i\u00e7in neden kritiktir<\/strong><\/h3>\n
    \n
  • Performans:<\/strong> Daha d\u00fc\u015f\u00fck hesaplama maliyetiyle y\u00fcksek do\u011fruluk sa\u011flar. <\/li>\n
  • G\u00fcvenilirlik:<\/strong> Ba\u011flam ba\u011f\u0131ml\u0131 kararlar \u00fcretir, hatal\u0131 tahmin oran\u0131n\u0131 d\u00fc\u015f\u00fcr\u00fcr. <\/li>\n
  • Maliyet:<\/strong> Ayn\u0131 veri setinden daha verimli bilgi \u00e7\u0131kar\u0131m\u0131, e\u011fitim maliyetini azalt\u0131r. <\/li>\n
  • \u00d6l\u00e7ekleme:<\/strong> B\u00fcy\u00fck modellerde paralel hesaplama imkan\u0131 sunar. <\/li>\n
  • Otomasyon:<\/strong> Metin analizinden veri s\u0131n\u0131fland\u0131rmaya kadar s\u00fcre\u00e7leri h\u0131zland\u0131r\u0131r. <\/li>\n
  • Karar alma:<\/strong> Karma\u015f\u0131k girdiler aras\u0131nda \u00f6nceliklendirilmi\u015f dikkat kurar. <\/li>\n
  • Operasyonel verimlilik:<\/strong> Kurumsal verilerin anlamland\u0131r\u0131lmas\u0131nda do\u011frulu\u011fu art\u0131r\u0131r.<\/li>\n<\/ul>\n
    \n

    Bu kavram NeKu.AI i\u00e7inde nas\u0131l uygulan\u0131r<\/strong><\/h3>\n

    NeKu.AI, yapay zeka ve otomasyon sistemlerinde self attention prensibini, \u00f6zellikle veri entegrasyon ve i\u015flem ak\u0131\u015flar\u0131nda kullan\u0131r. Workflow orkestrasyonlar\u0131nda n8n tabanl\u0131 s\u00fcre\u00e7lerde, giri\u015f verisi analiz edilirken hangi ad\u0131m\u0131n di\u011ferine daha \u00e7ok ba\u011fl\u0131 oldu\u011funu belirlemek bu mekanizmayla m\u00fcmk\u00fcnd\u00fcr. B\u00f6ylece model, SAP entegrasyonlar\u0131nda veri ba\u011flam\u0131n\u0131 daha tutarl\u0131 \u015fekilde korur.<\/p>\n

    \n

    AI geli\u015ftiricileri, \u00fcr\u00fcn y\u00f6neticileri, SAP dan\u0131\u015fmanlar\u0131 i\u00e7in ger\u00e7ek bir senaryo<\/strong><\/h3>\n
      \n
    1. Sorun:<\/strong> Kurumsal ERP sisteminde m\u00fc\u015fteri taleplerinin do\u011fru s\u0131n\u0131fland\u0131r\u0131lamamas\u0131. <\/li>\n
    2. Ba\u011flam:<\/strong> SAP verileri farkl\u0131 kaynaklardan gelir ve metin tabanl\u0131 a\u00e7\u0131klamalar tutars\u0131zd\u0131r. <\/li>\n
    3. Kavram\u0131n uygulanmas\u0131:<\/strong> Self attention mekanizmas\u0131, veri i\u00e7indeki anahtar kelimeler aras\u0131ndaki ili\u015fkileri hesaplayarak en anlaml\u0131 ba\u011flam\u0131 \u00e7\u0131kar\u0131r. <\/li>\n
    4. Sonu\u00e7:<\/strong> Talep s\u0131n\u0131fland\u0131rma do\u011frulu\u011fu y\u00fckselir, i\u015flem s\u00fcreleri azal\u0131r. <\/li>\n
    5. \u0130\u015f etkisi:<\/strong> M\u00fc\u015fteri memnuniyeti artar, sistem yan\u0131t s\u00fcresi d\u00fc\u015fer ve operasyonel maliyet optimize edilir.<\/li>\n<\/ol>\n
      \n

      S\u0131k yap\u0131lan hatalar ve en iyi uygulamalar<\/strong><\/h3>\n
        \n
      • A\u015f\u0131r\u0131 b\u00fcy\u00fck dikkat matrisleri e\u011fitimde bellek hatalar\u0131na neden olur; ba\u015fl\u0131k say\u0131s\u0131 dengelenmelidir. <\/li>\n
      • Dropout oran\u0131n\u0131 \u00e7ok d\u00fc\u015f\u00fck tutmak, modelin a\u015f\u0131r\u0131 \u00f6\u011frenmesine yol a\u00e7abilir. <\/li>\n
      • Performans i\u00e7in GPU veya TPU optimizasyonu \u015fartt\u0131r. <\/li>\n
      • En iyi uygulama olarak, self attention katmanlar\u0131n\u0131n erken evrelerde g\u00f6rselle\u015ftirilebilir sonu\u00e7lar\u0131 test etmek model a\u00e7\u0131klanabilirli\u011fini art\u0131r\u0131r.<\/li>\n<\/ul>\n
        \n

        Sonu\u00e7<\/strong><\/h3>\n

        Self attention, modern yapay zeka mimarilerinin merkezinde yer al\u0131r ve modellerin ba\u011flam\u0131 do\u011fru anlamas\u0131n\u0131 sa\u011flar. Hem teknik hem operasyonel seviyede verimlili\u011fi art\u0131r\u0131r ve \u00f6l\u00e7eklenebilir sistemlerin temel ta\u015f\u0131n\u0131 olu\u015fturur. NeKu.AI\u2019nin Temel Kavramlar serisinde bu mekanizma, ak\u0131ll\u0131 entegrasyon ve otomasyon s\u00fcre\u00e7lerinin daha bilin\u00e7li tasarlanmas\u0131na katk\u0131 sa\u011flar.<\/p>","protected":false},"excerpt":{"rendered":"

        Self attention nedir Giri\u015f Self attention, yapay zekada \u00f6zellikle b\u00fcy\u00fck dil modelleri (LLM) mimarilerinin temel bile\u015fenlerinden biridir. Bir modelin girdideki her \u00f6\u011fe aras\u0131nda ili\u015fkileri anlamas\u0131n\u0131 sa\u011flar [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":446,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[],"tags":[],"class_list":["post-445","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry"],"yoast_head":"\nSelf Attention ile Yapay Zeka Modellerinde Ba\u011flam Anlamland\u0131rma - NeKu.AI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/neku.ai\/en\/self-attention-yapay-zeka\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Self Attention ile Yapay Zeka Modellerinde Ba\u011flam Anlamland\u0131rma - NeKu.AI\" \/>\n<meta property=\"og:description\" content=\"Self attention nedir Giri\u015f Self attention, yapay zekada \u00f6zellikle b\u00fcy\u00fck dil modelleri (LLM) mimarilerinin temel bile\u015fenlerinden biridir. Bir modelin girdideki her \u00f6\u011fe aras\u0131nda ili\u015fkileri anlamas\u0131n\u0131 sa\u011flar [\u2026]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/neku.ai\/en\/self-attention-yapay-zeka\/\" \/>\n<meta property=\"og:site_name\" content=\"NeKu.AI\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-04T17:00:22+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-04T17:00:47+00:00\" \/>\n<meta name=\"author\" content=\"Serkan \u00d6zcan\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Serkan \u00d6zcan\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/neku.ai\\\/self-attention-yapay-zeka\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/self-attention-yapay-zeka\\\/\"},\"author\":{\"name\":\"Serkan \u00d6zcan\",\"@id\":\"https:\\\/\\\/neku.ai\\\/#\\\/schema\\\/person\\\/cf640cfda3e16635fb740662d943e96b\"},\"headline\":\"Self Attention ile Yapay Zeka Modellerinde Ba\u011flam Anlamland\u0131rma\",\"datePublished\":\"2025-12-04T17:00:22+00:00\",\"dateModified\":\"2025-12-04T17:00:47+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/self-attention-yapay-zeka\\\/\"},\"wordCount\":907,\"publisher\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/neku.ai\\\/self-attention-yapay-zeka\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/neku.ai\\\/wp-content\\\/uploads\\\/2025\\\/12\\\/cover-image-445.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/neku.ai\\\/self-attention-yapay-zeka\\\/\",\"url\":\"https:\\\/\\\/neku.ai\\\/self-attention-yapay-zeka\\\/\",\"name\":\"Self Attention ile Yapay Zeka Modellerinde Ba\u011flam Anlamland\u0131rma - 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