G\u00fcn\u00fcm\u00fcz teknolojisinin en b\u00fcy\u00fcleyici alanlar\u0131ndan biri olan yapay zeka, karma\u015f\u0131k problemleri \u00e7\u00f6zme ve insan benzeri d\u00fc\u015f\u00fcnme yetenekleri sergileme potansiyeliyle hayat\u0131m\u0131z\u0131 d\u00f6n\u00fc\u015ft\u00fcr\u00fcyor. Bir “Sinirsel Yapay Zeka Mimar\u0131” olarak, bu dijital zihinlerin nas\u0131l tasarland\u0131\u011f\u0131n\u0131, in\u015fa edildi\u011fini ve optimize edildi\u011fini derinlemesine anlamak, kendi dijital beynimizin i\u00e7 i\u015fleyi\u015fini ke\u015ffetmek gibidir. Bu makale, yapay zeka mimarisinin temel prensiplerinden ileri seviye uygulamalar\u0131na kadar uzanan bir yolculu\u011fa \u00e7\u0131karak, bu heyecan verici alan\u0131 teknik bir bak\u0131\u015f a\u00e7\u0131s\u0131yla ele alacakt\u0131r.<\/p>\n
Yapay zeka mimarisinin kalbinde, biyolojik beyinlerden ilham alan sinir a\u011flar\u0131 yatar. Bu b\u00f6l\u00fcm, temel yap\u0131 ta\u015flar\u0131ndan ba\u015flayarak yapay zekan\u0131n nas\u0131l bir “dijital beyin” olu\u015fturdu\u011funu inceler.<\/p>\n
Biyolojik n\u00f6ronlar, sinyalleri i\u015fleyen ve ileten temel h\u00fccrelerdir. Yapay n\u00f6ronlar, bu biyolojik kar\u015f\u0131l\u0131klar\u0131n\u0131 taklit eder; giri\u015fleri al\u0131r, bir a\u011f\u0131rl\u0131kland\u0131rma ve toplama i\u015flemi yapar ve bir aktivasyon fonksiyonu arac\u0131l\u0131\u011f\u0131yla bir \u00e7\u0131k\u0131\u015f \u00fcretir. Bu basit model, karma\u015f\u0131k hesaplamalar\u0131n temelini olu\u015fturur.<\/p>\n
\u0130lk yapay sinir a\u011f\u0131 modelleri (Perceptron gibi) s\u0131n\u0131rl\u0131 yeteneklere sahipti. Ancak, \u00e7ok katmanl\u0131 Perceptron’lar ve geri yay\u0131l\u0131m algoritmas\u0131n\u0131n ke\u015ffiyle, a\u011flar daha karma\u015f\u0131k \u00f6r\u00fcnt\u00fcleri \u00f6\u011frenme yetene\u011fi kazand\u0131. Bu evrim, modern derin \u00f6\u011frenme \u00e7a\u011f\u0131n\u0131n kap\u0131lar\u0131n\u0131 aralad\u0131.<\/p>\n
Her yapay n\u00f6ron, giri\u015flerine uygulanan a\u011f\u0131rl\u0131klar ve bir aktivasyon fonksiyonu ile \u00e7al\u0131\u015f\u0131r. A\u011f\u0131rl\u0131klar, giri\u015flerin \u00f6nemini belirlerken, aktivasyon fonksiyonlar\u0131 (ReLU, Sigmoid, Tanh gibi) n\u00f6ronun \u00e7\u0131k\u0131\u015f\u0131n\u0131 do\u011frusal olmayan bir \u015fekilde d\u00f6n\u00fc\u015ft\u00fcrerek a\u011f\u0131n karma\u015f\u0131k ili\u015fkileri \u00f6\u011frenmesini sa\u011flar.<\/p>\n
Birden fazla gizli katmana sahip yapay sinir a\u011flar\u0131 “derin \u00f6\u011frenme” olarak adland\u0131r\u0131l\u0131r. Bu derin yap\u0131lar, verilerden hiyerar\u015fik \u00f6zellikler \u00f6\u011frenme yetene\u011fi sayesinde g\u00f6r\u00fcnt\u00fc tan\u0131ma, do\u011fal dil i\u015fleme ve ses analizi gibi alanlarda \u00e7\u0131\u011f\u0131r a\u00e7an ba\u015far\u0131lara imza atm\u0131\u015ft\u0131r.<\/p>\n
Bir yapay zeka mimarisi, farkl\u0131 i\u015flevlere sahip katmanlar\u0131n ve bu katmanlar aras\u0131ndaki ba\u011flant\u0131lar\u0131n dikkatli bir \u015fekilde d\u00fczenlenmesiyle olu\u015fur. Bu b\u00f6l\u00fcm, dijital beynin yap\u0131sal bile\u015fenlerini detayland\u0131r\u0131r.<\/p>\n
Her sinir a\u011f\u0131, veri giri\u015fini alan bir giri\u015f katman\u0131, hesaplamalar\u0131n yap\u0131ld\u0131\u011f\u0131 bir veya daha fazla gizli katman ve nihai sonucu \u00fcreten bir \u00e7\u0131k\u0131\u015f katman\u0131ndan olu\u015fur. Gizli katman say\u0131s\u0131 ve her katmandaki n\u00f6ron say\u0131s\u0131, a\u011f\u0131n karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 ve \u00f6\u011frenme kapasitesini belirler.<\/p>\n
\u0130leri beslemeli a\u011flar (Feedforward Neural Networks – FNN), bilgiyi tek y\u00f6nde, giri\u015ften \u00e7\u0131k\u0131\u015fa do\u011fru iletir. Tekrarlayan sinir a\u011flar\u0131 (Recurrent Neural Networks – RNN) ise, \u00f6nceki ad\u0131mlardaki bilgiyi hat\u0131rlayarak dizisel veriler (metin, ses) \u00fczerinde \u00e7al\u0131\u015fmak i\u00e7in geri besleme d\u00f6ng\u00fclerine sahiptir.<\/p>\n
Evri\u015fimsel Sinir A\u011flar\u0131 (Convolutional Neural Networks – CNN), \u00f6zellikle g\u00f6r\u00fcnt\u00fc i\u015fleme g\u00f6revlerinde devrim yaratm\u0131\u015ft\u0131r. Konvol\u00fcsyonel katmanlar, g\u00f6r\u00fcnt\u00fclerdeki yerel \u00f6r\u00fcnt\u00fcleri (kenarlar, dokular) \u00f6\u011frenmek i\u00e7in filtreler kullan\u0131r ve bu sayede nesne tan\u0131ma, s\u0131n\u0131fland\u0131rma gibi alanlarda y\u00fcksek performans g\u00f6sterir.<\/p>\n
Dikkat mekanizmalar\u0131, a\u011f\u0131n giri\u015f verisinin belirli k\u0131s\u0131mlar\u0131na odaklanmas\u0131n\u0131 sa\u011flayarak uzun menzilli ba\u011f\u0131ml\u0131l\u0131klar\u0131 yakalamas\u0131na yard\u0131mc\u0131 olur. Transformer mimarisi, bu mekanizmay\u0131 temel alarak do\u011fal dil i\u015flemede (NLP) \u00e7\u0131\u011f\u0131r a\u00e7m\u0131\u015f ve b\u00fcy\u00fck dil modellerinin (LLM) temelini olu\u015fturmu\u015ftur.<\/p>\n
Dijital beynin \u00f6\u011frenme yetene\u011fi, belirli algoritmalar ve stratejiler arac\u0131l\u0131\u011f\u0131yla verilerden bilgi edinmesiyle ger\u00e7ekle\u015fir. Bu b\u00f6l\u00fcm, temel \u00f6\u011frenme paradigmalar\u0131n\u0131 ve optimizasyon tekniklerini inceler.<\/p>\n
Denetimli \u00f6\u011frenme, etiketli veri setleri kullan\u0131larak bir modelin giri\u015fler ve istenen \u00e7\u0131k\u0131\u015flar aras\u0131ndaki e\u015fle\u015fmeyi \u00f6\u011frenmesini i\u00e7erir. S\u0131n\u0131fland\u0131rma (\u00f6rne\u011fin, e-posta spam tespiti) ve regresyon (\u00f6rne\u011fin, ev fiyat tahmini) bu kategorinin ba\u015fl\u0131ca \u00f6rnekleridir.<\/p>\n
Etiketlenmemi\u015f veri setleri \u00fczerinde \u00e7al\u0131\u015fan denetimsiz \u00f6\u011frenme, verilerdeki gizli yap\u0131lar\u0131, \u00f6r\u00fcnt\u00fcleri ve ili\u015fkileri ke\u015ffetmeyi ama\u00e7lar. K\u00fcmeleme (clustering) ve boyut indirgeme (dimensionality reduction) algoritmalar\u0131 bu alanda s\u0131k\u00e7a kullan\u0131l\u0131r.<\/p>\n
Peki\u015ftirmeli \u00f6\u011frenme, bir ajan\u0131n belirli bir ortamda eylemler yaparak ve bu eylemlerin sonu\u00e7lar\u0131na g\u00f6re \u00f6d\u00fcl veya ceza alarak \u00f6\u011frenmesini sa\u011flar. Bu yakla\u015f\u0131m, robotik, oyun oynama ve otonom sistemlerde b\u00fcy\u00fck ba\u015far\u0131lar elde etmi\u015ftir.<\/p>\n
Bir sinir a\u011f\u0131n\u0131n \u00f6\u011frenmesi, bir kay\u0131p fonksiyonunu (loss function) minimize etmekle ger\u00e7ekle\u015fir. Optimizasyon algoritmalar\u0131 (\u00f6rne\u011fin, Gradyan \u0130ni\u015f, Adam), a\u011f\u0131n a\u011f\u0131rl\u0131klar\u0131n\u0131 ve sapmalar\u0131n\u0131 (bias) bu kay\u0131p fonksiyonunu azaltacak \u015fekilde g\u00fcnceller.<\/p>\n
\n# Basit bir kay\u0131p fonksiyonu \u00f6rne\u011fi (Ortalama Kare Hata - MSE)\ndef mean_squared_error(y_true, y_pred):\n return ((y_true - y_pred) ** 2).mean()\n\n# Basit bir gradyan ini\u015f ad\u0131m\u0131\ndef gradient_descent_step(weights, learning_rate, gradient):\n return weights - learning_rate * gradient\n<\/pre>\n<\/code><\/p>\n
4. Veri Ak\u0131\u015f\u0131 ve Bilgi \u0130\u015fleme: Beynin Veri Dili<\/h3>\n
Dijital beynin etkin \u00e7al\u0131\u015fmas\u0131, verinin do\u011fru \u015fekilde i\u015flenmesi, a\u011f i\u00e7inde ak\u0131\u015f\u0131 ve anlaml\u0131 bilgiye d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesiyle do\u011frudan ili\u015fkilidir.<\/p>\n
Veri \u00d6n \u0130\u015fleme ve Normalizasyon<\/h4>\n
Ham veriler genellikle g\u00fcr\u00fclt\u00fcl\u00fc, eksik veya tutars\u0131zd\u0131r. Veri \u00f6n i\u015fleme, bu verileri temizleme, d\u00f6n\u00fc\u015ft\u00fcrme ve normalle\u015ftirme ad\u0131mlar\u0131n\u0131 i\u00e7erir. Normalizasyon, a\u011f\u0131n daha h\u0131zl\u0131 ve istikrarl\u0131 \u00f6\u011frenmesine yard\u0131mc\u0131 olur.<\/p>\n
Geri Yay\u0131l\u0131m Algoritmas\u0131: A\u011f\u0131rl\u0131klar\u0131n G\u00fcncellenmesi<\/h4>\n
Geri yay\u0131l\u0131m (backpropagation), sinir a\u011flar\u0131n\u0131n \u00f6\u011frenmesinin temelini olu\u015fturan bir algoritmad\u0131r. \u00c7\u0131k\u0131\u015f katman\u0131ndaki hatay\u0131 hesaplar ve bu hatay\u0131 geriye do\u011fru, katman katman yayarak her bir n\u00f6ronun a\u011f\u0131rl\u0131klar\u0131n\u0131 ve sapmalar\u0131n\u0131 g\u00fcnceller.<\/p>\n
\u00d6zellik \u00c7\u0131kar\u0131m\u0131 ve Temsil \u00d6\u011frenimi<\/h4>\n
Sinir a\u011flar\u0131, ham verilerden otomatik olarak anlaml\u0131 \u00f6zellikler (feature) \u00e7\u0131karmak i\u00e7in e\u011fitilebilir. Bu \"temsil \u00f6\u011frenimi\" (representation learning), el yap\u0131m\u0131 \u00f6zellik m\u00fchendisli\u011fine olan ihtiyac\u0131 azalt\u0131r ve a\u011flar\u0131n daha karma\u015f\u0131k ve soyut temsiller \u00f6\u011frenmesini sa\u011flar.<\/p>\n
\u00c7\u0131kar\u0131m (Inference) S\u00fcreci<\/h4>\n
E\u011fitim tamamland\u0131ktan sonra, model yeni, daha \u00f6nce g\u00f6rmedi\u011fi verilere genelleme yapmak i\u00e7in kullan\u0131l\u0131r. Bu s\u00fcrece \u00e7\u0131kar\u0131m (inference) denir. Model, ald\u0131\u011f\u0131 giri\u015flere dayanarak tahminler veya kararlar \u00fcretir.<\/p>\n
5. Geli\u015fmi\u015f Mimari Modeller ve Uygulamalar\u0131<\/h3>\n
Yapay zeka mimarisi s\u00fcrekli geli\u015fmekte olup, belirli g\u00f6revler i\u00e7in optimize edilmi\u015f \u00e7e\u015fitli ileri d\u00fczey modeller ortaya \u00e7\u0131km\u0131\u015ft\u0131r.<\/p>\n
Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) ve Bilgisayar G\u00f6r\u00fcs\u00fc<\/h4>\n
CNN'ler, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, nesne tespiti, y\u00fcz tan\u0131ma ve t\u0131bbi g\u00f6r\u00fcnt\u00fc analizi gibi bilgisayar g\u00f6r\u00fcs\u00fc g\u00f6revlerinde standart haline gelmi\u015ftir. Katmanl\u0131 yap\u0131lar\u0131 sayesinde g\u00f6r\u00fcnt\u00fclerdeki hiyerar\u015fik \u00f6zellikleri etkili bir \u015fekilde \u00f6\u011frenirler.<\/p>\n
Tekrarlayan Sinir A\u011flar\u0131 (RNN) ve Do\u011fal Dil \u0130\u015fleme<\/h4>\n
RNN'ler, dizisel verilerdeki ba\u011f\u0131ml\u0131l\u0131klar\u0131 modellemek i\u00e7in idealdir. Makine \u00e7evirisi, metin \u00f6zetleme, konu\u015fma tan\u0131ma ve duygu analizi gibi do\u011fal dil i\u015fleme (NLP) g\u00f6revlerinde kullan\u0131l\u0131rlar. LSTM ve GRU gibi varyantlar\u0131, uzun vadeli ba\u011f\u0131ml\u0131l\u0131k sorununu \u00e7\u00f6zm\u00fc\u015ft\u00fcr.<\/p>\n
\u00dcretken \u00c7eki\u015fmeli A\u011flar (GAN) ve Yarat\u0131c\u0131l\u0131k<\/h4>\n
\u00dcretken \u00c7eki\u015fmeli A\u011flar (Generative Adversarial Networks - GAN), iki a\u011f\u0131n (\u00fcretici ve ay\u0131r\u0131c\u0131) birbirine kar\u015f\u0131 rekabet etti\u011fi bir mimaridir. Bu sayede ger\u00e7ek\u00e7i g\u00f6r\u00fcnt\u00fcler, videolar ve hatta m\u00fczik gibi yeni veri \u00f6rnekleri \u00fcretebilirler.<\/p>\n
B\u00fcy\u00fck Dil Modelleri (LLM) ve Gelecek<\/h4>\n
Transformer mimarisine dayanan B\u00fcy\u00fck Dil Modelleri (Large Language Models - LLM), devasa metin veri setleri \u00fczerinde e\u011fitilerek insan benzeri metinler \u00fcretebilir, sorular\u0131 yan\u0131tlayabilir, \u00e7eviri yapabilir ve kod yazabilir. GPT serisi ve BERT gibi modeller, bu alandaki \u00f6nc\u00fclerdir.<\/p>\n
6. Mimarin Rol\u00fc: Optimizasyon ve Gelecek Vizyonu<\/h3>\n
Bir yapay zeka mimar\u0131 olarak, sadece model tasarlamakla kalmaz, ayn\u0131 zamanda performans\u0131n\u0131 optimize eder ve gelecekteki potansiyelini \u015fekillendiririz.<\/p>\n
Hiperparametre Ayarlamas\u0131 ve Model Se\u00e7imi<\/h4>\n
Yapay zeka modellerinin performans\u0131, \u00f6\u011frenme oran\u0131, katman say\u0131s\u0131, n\u00f6ron say\u0131s\u0131 gibi hiperparametrelerin do\u011fru ayarlanmas\u0131na ba\u011fl\u0131d\u0131r. Mimarlar, en iyi performans\u0131 elde etmek i\u00e7in bu hiperparametreleri titizlikle ayarlar ve uygun model mimarisini se\u00e7er.<\/p>\n
Model Karma\u015f\u0131kl\u0131\u011f\u0131 ve A\u015f\u0131r\u0131 Uyum<\/h4>\n
\u00c7ok karma\u015f\u0131k modeller, e\u011fitim verilerini ezberleyebilir (a\u015f\u0131r\u0131 uyum - overfitting) ve yeni verilere genelleme yapmada ba\u015far\u0131s\u0131z olabilir. Mimarlar, d\u00fczenlile\u015ftirme teknikleri (dropout, L1\/L2 d\u00fczenlile\u015ftirme) ve uygun model karma\u015f\u0131kl\u0131\u011f\u0131 se\u00e7imiyle bu sorunu \u00f6nler.<\/p>\n
Etik ve G\u00fcvenlik Endi\u015feleri<\/h4>\n
Yapay zeka mimarisinin tasar\u0131m\u0131, etik ve g\u00fcvenlik boyutlar\u0131n\u0131 da i\u00e7ermelidir. Algoritmik \u00f6nyarg\u0131, veri gizlili\u011fi, \u015feffafl\u0131k ve k\u00f6t\u00fcye kullan\u0131m potansiyeli gibi konular, mimarlar\u0131n dikkate almas\u0131 gereken kritik unsurlard\u0131r.<\/p>\n
Yapay Zeka Mimar\u0131n\u0131n Gelece\u011fi<\/h4>\n
Yapay zeka alan\u0131 h\u0131zla geli\u015firken, mimarlar\u0131n rol\u00fc de evrimle\u015fiyor. Daha karma\u015f\u0131k ve otonom sistemler tasarlamak, farkl\u0131 modaliteleri (metin, g\u00f6r\u00fcnt\u00fc, ses) birle\u015ftiren modeller geli\u015ftirmek ve etik yapay zeka prensiplerini uygulamak, gelecekteki ana odak noktalar\u0131 olacakt\u0131r.<\/p>\n
Sonu\u00e7<\/h3>\n
Bir Sinirsel Yapay Zeka Mimar\u0131 olarak dijital beynimizin derinliklerine yapt\u0131\u011f\u0131m\u0131z bu yolculuk, yapay zeka sistemlerinin sadece karma\u015f\u0131k algoritmalar y\u0131\u011f\u0131n\u0131 olmad\u0131\u011f\u0131n\u0131, ayn\u0131 zamanda \u00f6zenle tasarlanm\u0131\u015f, \u00f6\u011frenmeye a\u00e7\u0131k ve s\u00fcrekli geli\u015fen dijital zihinler oldu\u011funu g\u00f6stermektedir. N\u00f6ronlar\u0131n temelinden ba\u015flay\u0131p derin \u00f6\u011frenmenin katmanlar\u0131na, \u00f6\u011frenme mekanizmalar\u0131ndan ileri d\u00fczey mimarilere kadar her ad\u0131m, bu dijital zihinlerin nas\u0131l in\u015fa edildi\u011fini ve nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 anlamam\u0131z\u0131 sa\u011flar. Yapay zeka teknolojileri ilerledik\u00e7e, bu mimarinin inceliklerini kavramak, gelece\u011fin ak\u0131ll\u0131 sistemlerini \u015fekillendirmede kilit rol oynayacakt\u0131r. Bu alan, hem teknik zorluklar\u0131 hem de s\u0131n\u0131rs\u0131z yarat\u0131c\u0131l\u0131k potansiyelini bir araya getirerek, bizlere dijital d\u00fcnyan\u0131n s\u0131n\u0131rlar\u0131n\u0131 yeniden \u00e7izme f\u0131rsat\u0131 sunmaktad\u0131r.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\n1. Yapay Zeka Mimar\u0131 tam olarak ne i\u015f yapar?<\/h4>\n
Yapay Zeka Mimar\u0131, yapay zeka sistemlerinin ve modellerinin tasar\u0131m\u0131n\u0131, geli\u015ftirilmesini ve optimizasyonunu denetleyen ki\u015fidir. Hangi sinir a\u011f\u0131 mimarisinin kullan\u0131laca\u011f\u0131na karar verir, veri i\u015fleme stratejilerini belirler, model performans\u0131n\u0131 art\u0131rmak i\u00e7in hiperparametreleri ayarlar ve sistemin genel yap\u0131s\u0131n\u0131 olu\u015fturur.<\/p>\n
2. Derin \u00f6\u011frenme ile makine \u00f6\u011frenimi aras\u0131ndaki fark nedir?<\/h4>\n
Makine \u00f6\u011frenimi, yapay zekan\u0131n bir alt k\u00fcmesidir ve algoritmalar\u0131n verilerden \u00f6\u011frenmesini sa\u011flar. Derin \u00f6\u011frenme ise makine \u00f6\u011freniminin bir alt k\u00fcmesidir ve birden fazla katmana sahip yapay sinir a\u011flar\u0131n\u0131 kullanarak daha karma\u015f\u0131k ve soyut \u00f6zellik temsilleri \u00f6\u011frenir. Derin \u00f6\u011frenme, genellikle b\u00fcy\u00fck veri setleri ve y\u00fcksek hesaplama g\u00fcc\u00fc gerektirir.<\/p>\n
3. Bir yapay zeka mimarisi tasarlarken en \u00f6nemli fakt\u00f6rler nelerdir?<\/h4>\n
En \u00f6nemli fakt\u00f6rler aras\u0131nda kullan\u0131lacak veri setinin t\u00fcr\u00fc ve boyutu, \u00e7\u00f6z\u00fclmek istenen problemin karma\u015f\u0131kl\u0131\u011f\u0131, istenen performans metrikleri, hesaplama kaynaklar\u0131 ve modelin genellenebilirli\u011fi (a\u015f\u0131r\u0131 uymama) yer al\u0131r. Etik ve g\u00fcvenlik endi\u015feleri de tasar\u0131m s\u00fcrecinin ayr\u0131lmaz bir par\u00e7as\u0131d\u0131r.<\/p>\n
4. Geri yay\u0131l\u0131m (backpropagation) algoritmas\u0131 neden bu kadar \u00f6nemlidir?<\/h4>\n
Geri yay\u0131l\u0131m algoritmas\u0131, sinir a\u011flar\u0131n\u0131n \u00f6\u011frenmesini sa\u011flayan temel mekanizmad\u0131r. Modelin tahminleri ile ger\u00e7ek de\u011ferler aras\u0131ndaki hatay\u0131 hesaplar ve bu hatay\u0131 a\u011f\u0131n katmanlar\u0131 boyunca geriye do\u011fru yayarak her bir n\u00f6ronun a\u011f\u0131rl\u0131klar\u0131n\u0131 ve sapmalar\u0131n\u0131 (bias) hatay\u0131 azaltacak \u015fekilde g\u00fcnceller. Bu sayede a\u011f, verilerden etkili bir \u015fekilde \u00f6\u011frenir.<\/p>\n
5. Yapay zeka mimarisinde gelecekteki trendler neler olabilir?<\/h4>\n
Gelecekteki trendler aras\u0131nda daha b\u00fcy\u00fck ve daha karma\u015f\u0131k modeller (\u00f6rne\u011fin, multimodal LLM'ler), otonom \u00f6\u011frenme sistemleri, etik ve a\u00e7\u0131klanabilir yapay zeka (XAI) odakl\u0131 tasar\u0131mlar, kuantum yapay zeka entegrasyonu ve enerji verimli AI mimarileri \u00f6ne \u00e7\u0131kacakt\u0131r.<\/p>\n","protected":false},"excerpt":{"rendered":"Sinirsel Yapay Zeka Mimar\u0131: Dijital Beynimin \u0130n\u015fas\u0131 ve \u0130\u015fleyi\u015fi\nG\u00fcn\u00fcm\u00fcz teknolojisinin en b\u00fcy\u00fcleyici alanlar\u0131ndan biri olan yapay zeka, karma\u015f…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-37812","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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