{"id":1008876,"date":"2023-09-12T14:00:00","date_gmt":"2023-09-12T21:00:00","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/yapay-zeka-modeli-nedir-turleri-sentetik-verinin-rolu\/"},"modified":"2025-02-13T02:05:29","modified_gmt":"2025-02-13T09:05:29","slug":"yapay-zeka-modeli-nedir-turleri-sentetik-verinin-rolu","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/tr\/yapay-zeka-modeli-nedir-turleri-sentetik-verinin-rolu\/","title":{"rendered":"Yapay Zeka Modeli: Nedir, T\u00fcrleri + Sentetik Verinin Rol\u00fc"},"content":{"rendered":"\n<p>S\u00fcrekli de\u011fi\u015fen teknoloji d\u00fcnyas\u0131nda, yapay zeka modellerinin kullan\u0131m\u0131 giderek daha yayg\u0131n hale geliyor. Bir veri bilimcisi olarak ne kadar deneyimli olursan\u0131z olun veya yapay zeka d\u00fcnyas\u0131nda ne kadar yeni olursan\u0131z olun, bir yapay zeka modelinin ne oldu\u011funu ve farkl\u0131 kullan\u0131mlar\u0131n\u0131 bilmek \u00f6nemlidir. <\/p>\n\n<p>Bu blogda, \u00e7e\u015fitli t\u00fcrlerini, farkl\u0131 kullan\u0131mlar\u0131n\u0131 ve sentetik verilerin geli\u015ftirme ve de\u011ferlendirmede oynad\u0131\u011f\u0131 kritik rol\u00fc inceleyerek yapay zeka modelini ke\u015ffedece\u011fiz.<\/p>\n\n<h2 class=\"wp-block-heading\">Yapay zeka modeli nedir?<\/h2>\n\n<p>YZ veya Yapay Zeka, bilgisayarlarda ve di\u011fer teknolojilerde insan zekas\u0131n\u0131n sim\u00fclasyonunu ifade eder. Bir YZ modeli, YZ ve makine \u00f6\u011frenimi sistemlerinin temel bir bile\u015fenidir. YZ sistemleri, tahminlerde bulunmak, kararlar almak veya belirli g\u00f6revleri yerine getirmek i\u00e7in bunu hesaplamal\u0131 bir temsil olarak kullan\u0131r.  <\/p>\n\n<p>Yapay zeka modelleri, inovasyonu y\u00f6nlendiren motordur. Bu <a href=\"https:\/\/www.questionpro.com\/blog\/generative-models\/\">\u00fcretken modeller<\/a>, sofistike veri modellerini analiz etmek i\u00e7in bilgisayarla g\u00f6rme, do\u011fal dil i\u015fleme ve makine \u00f6\u011frenimini kullan\u0131r. <\/p>\n\n<p>Bununla birlikte, \u00f6r\u00fcnt\u00fc tan\u0131man\u0131n \u00f6tesinde, yapay zeka modelleri \u00f6\u011frenmek i\u00e7in karar verme algoritmalar\u0131n\u0131 kullan\u0131r. Bir e\u011fitim, veri toplama ve veri analizi s\u00fcreci arac\u0131l\u0131\u011f\u0131yla kendilerine atanan faaliyet ve hedeflerde ustala\u015fmaya yakla\u015f\u0131yorlar. <\/p>\n\n<p>Yapay zeka modeli, zorlu sorunlar\u0131 \u00e7\u00f6zmemizde bize yard\u0131mc\u0131 olan s\u00fcper kahramand\u0131r. B\u00fcy\u00fck miktarda veriyi i\u015flemeyi ve karma\u015f\u0131k sorunlar\u0131 \u00e7\u00f6zmeyi ba\u015far\u0131r. \u0130ster internetteki en sevimli kedi yavrusu resimlerini bulmak ister hava durumunu tahmin etmek olsun, YZ modelleri her zaman m\u00fckemmellik i\u00e7in \u00e7abalayan sorun \u00e7\u00f6z\u00fcc\u00fclerimizdir.  <\/p>\n\n<h2 class=\"wp-block-heading\">YZ modellerinin t\u00fcrleri<\/h2>\n\n<p>Yapay zeka, \u00e7ok say\u0131da yapay zeka modelinin bulundu\u011fu geni\u015f bir aland\u0131r. Her YZ modeli belirli i\u015fler ve zorluklar i\u00e7in en uygun olan\u0131d\u0131r. \u015eimdi en yayg\u0131n YZ modellerinden baz\u0131lar\u0131na ve nas\u0131l \u00e7al\u0131\u015ft\u0131klar\u0131na daha yak\u0131ndan bakal\u0131m.  <\/p>\n\n<h3 class=\"wp-block-heading\">01. Derin sinir a\u011flar\u0131<\/h3>\n\n<p>Derin sinir a\u011flar\u0131 (DNN&#8217;ler) \u015fu anda mevcut olan en b\u00fcy\u00fcleyici ve devrim niteli\u011findeki yapay zeka modelleri aras\u0131ndad\u0131r. Derin \u00f6\u011frenme modelinin tasar\u0131m\u0131 insan beynine dayan\u0131r ve birbirine ba\u011fl\u0131 d\u00fc\u011f\u00fcmlerin veya n\u00f6ronlar\u0131n katmanlar\u0131ndan olu\u015fur. &#8220;Derin&#8221; k\u0131sm\u0131, girdiden sofistike kal\u0131plar\u0131 ve temsilleri \u00f6\u011frenmelerini sa\u011flayan birden fazla katmana sahip olmay\u0131 ifade eder.  <\/p>\n\n<p>Bu yapay sinir a\u011flar\u0131n\u0131n temel yeteneklerinden biri, ham verilerden otomatik olarak \u00f6zellikler \u00e7\u0131karabilmeleridir. A\u011f katmanlar aras\u0131nda ilerledik\u00e7e, bu \u00f6zellikler giderek daha soyut hale gelir ve bu da ayr\u0131nt\u0131l\u0131 desenleri ve temsilleri tan\u0131mas\u0131n\u0131 sa\u011flar. <\/p>\n\n<p>DNN son derece uyarlanabilirdir ve bu yapay zeka modelini g\u00f6r\u00fcnt\u00fc ve ses tan\u0131ma, do\u011fal dil i\u015fleme, otonom s\u00fcr\u00fc\u015f, oyun oynama ve daha fazlas\u0131 gibi \u00e7e\u015fitli g\u00f6revler i\u00e7in kullanabilirsiniz. Bilgisayarla g\u00f6rme ve konu\u015fma sentezi gibi alanlara \u00f6nemli katk\u0131larda bulunmu\u015ftur. <\/p>\n\n<h3 class=\"wp-block-heading\">02. Lojistik regresyon yapay zeka modeli<\/h3>\n\n<p>Lojistik regresyon, ikili s\u0131n\u0131fland\u0131rma uygulamalar\u0131 i\u00e7in yayg\u0131n olarak kullan\u0131lan basit ve \u00e7ok y\u00f6nl\u00fc bir yapay zeka modelidir. Kullan\u0131m kolayl\u0131\u011f\u0131 ve etkinli\u011fi, onu sa\u011fl\u0131k, pazarlama, finans ve di\u011ferleri dahil olmak \u00fczere \u00e7e\u015fitli sekt\u00f6rlerde pop\u00fcler bir se\u00e7im haline getirmektedir. <\/p>\n\n<p>Lojistik regresyon modelinin amac\u0131 ikili kararlar vermektir. Sorgulara &#8220;Evet&#8221; veya &#8220;Hay\u0131r&#8221;, &#8220;Spam&#8221; veya &#8220;Spam De\u011fil&#8221; veya &#8220;Varsay\u0131lan&#8221; veya &#8220;Varsay\u0131lan Yok&#8221; \u015feklinde yan\u0131t verir. \u00d6rne\u011fin, \u00e7e\u015fitli fakt\u00f6rlere ba\u011fl\u0131 olarak bir e-postan\u0131n spam olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in kullanabilirsiniz.  <\/p>\n\n<h3 class=\"wp-block-heading\">03. Do\u011frusal regresyon modeli<\/h3>\n\n<p>Do\u011frusal regresyon, bir veya daha fazla ba\u011f\u0131ms\u0131z \u00f6zellik ile ba\u011f\u0131ml\u0131 de\u011fi\u015fken aras\u0131ndaki do\u011frusal ba\u011flant\u0131y\u0131 belirleyen bir t\u00fcr kontroll\u00fc makine \u00f6\u011frenimi modelidir. Girdi ve \u00e7\u0131kt\u0131 de\u011fi\u015fkenleri aras\u0131nda bir ili\u015fki arar. <\/p>\n\n<p>Bu teknik, ba\u011f\u0131ms\u0131z de\u011fi\u015fkenlere ba\u011fl\u0131 olarak ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin de\u011ferini tahmin edebilen en uygun do\u011frusal denklemi arar. Denklem, ba\u011f\u0131ml\u0131 ve ba\u011f\u0131ms\u0131z de\u011fi\u015fkenler aras\u0131ndaki ili\u015fkiyi d\u00fcz bir \u00e7izgi ile temsil eder. <\/p>\n\n<p>Do\u011frusal regresyon modeli, veri noktalar\u0131ndan olu\u015fan bir da\u011f\u0131l\u0131m grafi\u011fi \u00fczerinden d\u00fcz bir \u00e7izgi \u00e7izmek gibidir. Metrekare ve konum gibi \u00f6zelliklere ba\u011fl\u0131 olarak m\u00fclk de\u011ferlerini tahmin etmek i\u00e7in kullan\u0131\u015fl\u0131d\u0131r. <\/p>\n\n<h3 class=\"wp-block-heading\">04. Karar a\u011fa\u00e7lar\u0131 yapay zeka modeli<\/h3>\n\n<p>Yapay zeka i\u00e7in karar a\u011fa\u00e7lar\u0131 ak\u0131\u015f \u015femalar\u0131na benzer. E\u011fer-\u015fimdi-\u00f6teki kriterlerinden olu\u015fan bir if-then-else \u00e7er\u00e7evesi kullanarak karar verirler. Karar a\u011fa\u00e7lar\u0131, bir ki\u015finin ya\u015f\u0131na, gelirine ve tarama ge\u00e7mi\u015fine g\u00f6re bir \u00fcr\u00fcn\u00fc sat\u0131n alma olas\u0131l\u0131\u011f\u0131n\u0131n olup olmad\u0131\u011f\u0131n\u0131 tahmin etmek gibi art arda kararlar vermemizi gerektiren i\u015flerde s\u0131kl\u0131kla kullan\u0131l\u0131r.  <\/p>\n\n<p>Karar a\u011fac\u0131, bir karara varmak i\u00e7in \u00f6nceki kararlardan elde edilen mevcut ger\u00e7ekleri analiz eder. Bu a\u011fa\u00e7lar s\u0131kl\u0131kla bir e\u011fer\/o zaman yap\u0131s\u0131n\u0131 takip eder. \u00d6rne\u011fin, evde bir sandvi\u00e7 yaparsan\u0131z, \u00f6\u011fle yeme\u011fi sat\u0131n almak zorunda kalmazs\u0131n\u0131z.  <\/p>\n\n<p>Karar a\u011fa\u00e7lar\u0131 modelini hem regresyon hem de s\u0131n\u0131fland\u0131rma problemlerini \u00e7\u00f6zmek i\u00e7in kullanabilirsiniz. Ayr\u0131ca, tahmine dayal\u0131 analiti\u011fin ilk s\u00fcr\u00fcmleri temel karar a\u011fa\u00e7lar\u0131 taraf\u0131ndan destekleniyordu. <\/p>\n\n<h3 class=\"wp-block-heading\">05. Rastgele orman modeli<\/h3>\n\n<p>Rastgele orman, karar a\u011fac\u0131 tabanl\u0131 bir makine \u00f6\u011frenimi modelidir. Bir karar a\u011fac\u0131n\u0131 bilgisayar bilimleri d\u00fcnyas\u0131ndaki ak\u0131ll\u0131 bir yard\u0131mc\u0131 olarak d\u00fc\u015f\u00fcn\u00fcn. \u015eimdi, bu yard\u0131mc\u0131lardan olu\u015fan bir grubun birlikte \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 hayal edin &#8211; i\u015fte bu rastgele bir ormand\u0131r. Bu ormanda her bir karar a\u011fac\u0131, bir tahmin veya se\u00e7im yapmak gibi kendi i\u015fini yapar.   <\/p>\n\n<p>Rastgele orman algoritmas\u0131 iki a\u015famaya ayr\u0131l\u0131r: rastgele orman olu\u015fturma ve ilk ad\u0131mda olu\u015fturulan rastgele orman s\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131n\u0131 kullanarak tahmin. En iyi bile\u015fen kar\u0131\u015f\u0131m\u0131n\u0131 belirlemek i\u00e7in t\u0131ptaki uygulama i\u00e7in rastgele orman modelini kullanabilirsiniz. <\/p>\n\n<h3 class=\"wp-block-heading\">06. K-en yak\u0131n kom\u015fu modeli<\/h3>\n\n<p>K-En Yak\u0131n Kom\u015fu (K-NN) tekni\u011fi, s\u0131n\u0131fland\u0131rma ve regresyon problemleri i\u00e7in basit ve zarif bir \u00e7\u00f6z\u00fcmd\u00fcr. Bu y\u00f6ntem, benzer \u00f6\u011felerin veya verilerin yak\u0131nlarda bulundu\u011fu fikri \u00fczerine \u00e7al\u0131\u015f\u0131r.   <\/p>\n\n<p>K-NN denetimli bir \u00f6\u011frenme modelidir, yani etiketli verilere dayal\u0131 tahminler yapar. Bu teknik, benzer \u00f6\u011felerin (verilerin) yak\u0131nlarda bulundu\u011fu varsay\u0131m\u0131na dayan\u0131r. G\u00fc\u00e7l\u00fc bir model olmas\u0131na ra\u011fmen, en \u00f6nemli dezavantajlar\u0131ndan biri veri hacmi artt\u0131k\u00e7a performans\u0131n d\u00fc\u015fmesidir.  <\/p>\n\n<h3 class=\"wp-block-heading\">07. Naive Bayes modeli<\/h3>\n\n<p>Naive Bayes modeli, Bayes teoremine dayanan olas\u0131l\u0131ksal bir yapay zeka modelidir. Model, bir \u00f6zelli\u011fin varl\u0131\u011f\u0131n\u0131n ba\u015fka bir \u00f6zelli\u011fin ortaya \u00e7\u0131kmas\u0131na ba\u011fl\u0131 olmad\u0131\u011f\u0131 varsay\u0131m\u0131na dayan\u0131r. Bu varsay\u0131m neredeyse hi\u00e7bir zaman do\u011fru olmad\u0131\u011f\u0131ndan, model &#8216;naif&#8217; olarak adland\u0131r\u0131l\u0131r.  <\/p>\n\n<p>Hem ikili hem de \u00e7oklu s\u0131n\u0131f s\u0131n\u0131fland\u0131rmalar\u0131 i\u00e7in kullanabilirsiniz. Spam e-posta tan\u0131mlama ve duygu analizi gibi do\u011fal dil i\u015fleme faaliyetlerinde s\u0131kl\u0131kla Naive Bayes kullan\u0131l\u0131r. <\/p>\n\n<h2 class=\"wp-block-heading\">Yapay zeka modeli uygulamalar\u0131<\/h2>\n\n<p>Bir yapay zeka modelinin d\u00fcnyada nereye oturdu\u011funu ve hayat\u0131n\u0131z\u0131n farkl\u0131 unsurlar\u0131n\u0131 nas\u0131l etkiledi\u011fini merak ediyor olabilirsiniz. \u0130\u015fte yapay zeka modellerinin bir\u00e7ok alanda nas\u0131l kullan\u0131ld\u0131\u011f\u0131na dair baz\u0131 \u00f6rnekler: <\/p>\n\n<ul>\n<li><strong>Sa\u011fl\u0131k hizmetleri:<\/strong> Yapay zeka modelleri doktorlar\u0131n hastal\u0131klar\u0131 te\u015fhis etmesine, ila\u00e7 ke\u015ffini h\u0131zland\u0131rmas\u0131na ve genel hasta bak\u0131m kalitesini art\u0131rmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n\n\n\n<li><strong>Finans: <\/strong>Doland\u0131r\u0131c\u0131l\u0131k faaliyetlerini tespit ederek, ticaret tekniklerini otomatikle\u015ftirerek ve yat\u0131r\u0131m portf\u00f6ylerini optimize ederek daha ak\u0131ll\u0131 finansal kararlar vermenize yard\u0131mc\u0131 olurlar.<\/li>\n\n\n\n<li><strong>Do\u011fal Dil \u0130\u015fleme: <\/strong>Bu yapay zeka modelleri, sohbet robotlar\u0131n\u0131n daha sorunsuz ileti\u015fim kurmas\u0131n\u0131, dil \u00e7evirisini kolayla\u015ft\u0131rmas\u0131n\u0131 ve metindeki duygular\u0131 de\u011ferlendirmesini sa\u011flayarak ileti\u015fimi ve m\u00fc\u015fteri hizmetlerini art\u0131r\u0131r.<\/li>\n\n\n\n<li><strong>G\u00f6r\u00fcnt\u00fc ve video analizi: <\/strong>Bu yapay zeka modelleri y\u00fcz g\u00f6r\u00fcnt\u00fcs\u00fc tan\u0131ma, nesne alg\u0131lama ve video g\u00f6zetimini destekleyerek g\u00fcvenlik ve i\u00e7erik analizini geli\u015ftirir.<\/li>\n\n\n\n<li><strong>\u00d6neri Sistemleri: <\/strong>Bu modeller, i\u00e7erik ve \u00fcr\u00fcn \u00f6nerilerinizi \u00f6zelle\u015ftirerek deneyiminizi daha e\u011flenceli ve verimli hale getirir.<\/li>\n\n\n\n<li><strong>\u00c7evresel \u0130zleme: <\/strong>Yapay Zeka modelleri, ya\u015fad\u0131\u011f\u0131n\u0131z \u00e7evre \u00fczerinde etkisi olan verileri analiz ederek iklim de\u011fi\u015fikli\u011fini \u00f6ng\u00f6rmeye, hava durumunu tahmin etmeye ve kirlili\u011fi azaltmaya yard\u0131mc\u0131 olur.<\/li>\n\n\n\n<li><strong>E\u011fitim: <\/strong>Yapay zeka modelleri \u00f6\u011frenme deneyimlerini ki\u015fiselle\u015ftirir, \u00f6\u011frenci ba\u015far\u0131s\u0131n\u0131 de\u011ferlendirir ve idari i\u015fleri otomatikle\u015ftirir, bu da sonu\u00e7ta hem e\u011fitmenlere hem de \u00f6\u011frencilere yard\u0131mc\u0131 olur.<\/li>\n<\/ul>\n\n<h2 class=\"wp-block-heading\">Yapay zeka modelleri ile makine \u00f6\u011frenimi modelleri aras\u0131ndaki fark<\/h2>\n\n<p>Yapay zeka (AI) ve makine \u00f6\u011frenimi (ML) bazen birlikte kullan\u0131lan ifadelerdir, ancak teknoloji ve <a href=\"https:\/\/www.questionpro.com\/blog\/tr\/veri-bilimi-yeni-baslayanlar-icin-eksiksiz-bir-kilavuz\/\">veri bilimi<\/a> d\u00fcnyalar\u0131nda farkl\u0131 kavramlar\u0131 temsil ederler. \u015eimdi farkl\u0131l\u0131klara bakal\u0131m: <\/p>\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Aspect<\/strong><\/td><td><strong>Yapay Zeka Modelleri<\/strong><\/td><td><strong>Makine \u00d6\u011frenimi Modelleri<\/strong><\/td><\/tr><tr><td>Kapsam ve Ama\u00e7<\/td><td>\u0130nsan benzeri zeka gerektiren geni\u015f bir g\u00f6rev yelpazesini kapsar.<\/td><td>Belirli g\u00f6revler i\u00e7in verilerden \u00f6\u011frenen algoritmalar geli\u015ftirmeye odaklan\u0131r.<\/td><\/tr><tr><td>\u00d6\u011frenme ve Adaptasyon<\/td><td>Veri odakl\u0131 \u00f6\u011frenme ile s\u0131n\u0131rl\u0131 olmay\u0131p sembolik ak\u0131l y\u00fcr\u00fctmeyi de i\u00e7erebilir.<\/td><td>\u00d6ncelikle verilerden kal\u0131plar\u0131 ve ili\u015fkileri \u00f6\u011frenmeye odaklanm\u0131\u015ft\u0131r.<\/td><\/tr><tr><td>Karma\u015f\u0131kl\u0131k ve \u00c7ok Y\u00f6nl\u00fcl\u00fck<\/td><td>Karma\u015f\u0131kl\u0131k ve \u00e7ok y\u00f6nl\u00fcl\u00fck a\u00e7\u0131s\u0131ndan b\u00fcy\u00fck farkl\u0131l\u0131klar g\u00f6sterir. Kural tabanl\u0131 sistemleri ve sinir a\u011flar\u0131n\u0131 i\u00e7erebilir. <\/td><td>Tipik olarak veri odakl\u0131d\u0131r, s\u0131n\u0131fland\u0131rma ve regresyon gibi g\u00f6revlerde uzmanla\u015fm\u0131\u015ft\u0131r.<\/td><\/tr><tr><td>\u00d6rnek Uygulamalar<\/td><td>Sanal ki\u015fisel asistanlar (\u00f6rn. Siri), genel ama\u00e7l\u0131 yapay zeka sistemleri (\u00f6rn. IBM&#8217;in Watson&#8217;\u0131).<\/td><td>\u00d6neri sistemleri, otonom ara\u00e7lar, doland\u0131r\u0131c\u0131l\u0131k tespiti ve sa\u011fl\u0131k hizmetleri (\u00f6rne\u011fin, t\u0131bbi g\u00f6r\u00fcnt\u00fc analizi).<\/td><\/tr><\/tbody><\/table><\/figure>\n\n<h2 class=\"wp-block-heading\">Sentetik veri ve yapay zeka modelindeki rol\u00fc<\/h2>\n\n<p><a href=\"https:\/\/www.questionpro.com\/blog\/tr\/sentetik-veri-nedir-turleri-yontemleri-kullanimi\/\">Sentetik veriler<\/a>, ger\u00e7ek d\u00fcnya verilerinin \u00f6zelliklerini kopyalayan yapay olarak olu\u015fturulmu\u015f verilerdir. Bu, orijinali nadir bulunan bir par\u00e7an\u0131n ger\u00e7e\u011fe yak\u0131n bir kopyas\u0131n\u0131 olu\u015fturmak gibidir. Metin, g\u00f6r\u00fcnt\u00fc ve say\u0131sal de\u011ferler sentetik veri \u00f6rnekleridir.  <\/p>\n\n<h3 class=\"wp-block-heading\">Yapay zeka modelinde sentetik verilerin rol\u00fc<\/h3>\n\n<ul>\n<li><strong>Veri Art\u0131rma: <\/strong>Sentetik veriler, YZ modelinizin performans\u0131n\u0131 art\u0131rmak i\u00e7in gizli silah\u0131n\u0131zd\u0131r. Ger\u00e7ek verilerinizle e\u015fle\u015fen daha fazla yapay olarak olu\u015fturulmu\u015f veri olu\u015fturarak, AI modelleriniz e\u011fitmek i\u00e7in daha fazla \u00f6rne\u011fe sahip olacak, genelleme ve etkinli\u011fi art\u0131racakt\u0131r. <\/li>\n\n\n\n<li><strong>Gizlilik ve G\u00fcvenlik: <\/strong>Hassas bilgilerin korunmas\u0131, g\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 ortam\u0131nda kritik \u00f6neme sahiptir. Yapay veriler, gizlili\u011finizden \u00f6d\u00fcn vermeden \u00e7al\u0131\u015fma veya geli\u015ftirme i\u00e7in veri payla\u015fman\u0131za olanak tan\u0131r. Gizli bilgileri a\u00e7\u0131\u011fa \u00e7\u0131karmadan istatistiksel \u00f6zellikleri koruyan sahte verilerle ba\u015fa \u00e7\u0131kabilirsiniz.  <\/li>\n\n\n\n<li><strong>Veri \u00c7e\u015fitlili\u011fi: <\/strong>Ger\u00e7ek d\u00fcnya verileri zaman zaman monoton g\u00f6r\u00fcnebilir. Ger\u00e7ekte nadir g\u00f6r\u00fclen veya yeterince temsil edilmeyen senaryolar veya veri noktalar\u0131 olu\u015fturmak i\u00e7in yapay olarak olu\u015fturulmu\u015f verileri kullanabilirsiniz. Bu \u00e7e\u015fitlilik, yapay zeka modellerinizin uyarlanabilirli\u011fini ve sa\u011flaml\u0131\u011f\u0131n\u0131 art\u0131r\u0131r.  <\/li>\n\n\n\n<li><strong>Maliyet Verimlili\u011fi: <\/strong>Ger\u00e7ek d\u00fcnyada veri toplama ve etiketleme zaman al\u0131c\u0131 ve maliyetli olabilir. \u00d6te yandan, yapay veriler h\u0131zl\u0131 ve ucuz bir \u015fekilde olu\u015fturulabilir, bu da yapay zeka modellerinizi e\u011fitmek ve test etmek i\u00e7in uygun maliyetli bir se\u00e7enek haline getirir. <\/li>\n\n\n\n<li><strong>Dengesiz Verilerin \u00dcstesinden Gelme:<\/strong> Bir s\u0131n\u0131f\u0131n di\u011ferlerinden \u00e7ok daha az \u00f6rne\u011fe sahip oldu\u011fu s\u0131n\u0131fland\u0131rma g\u00f6revlerinde dengesiz veri k\u00fcmeleriyle ba\u015fa \u00e7\u0131kmak zor olabilir. Sentetik veriler, modelinizin bask\u0131n s\u0131n\u0131f\u0131 tercih etmemesi i\u00e7in yetersiz temsil edilen s\u0131n\u0131ftan daha fazla \u00f6rnek sa\u011flayarak yard\u0131mc\u0131 olabilir. <\/li>\n<\/ul>\n\n<h2 class=\"wp-block-heading\">Yapay zeka modellerinin sentetik verilerle test edilmesi<\/h2>\n\n<p>Yapay zeka modelleri geli\u015ftirmek s\u00f6z konusu oldu\u011funda, test etmek ba\u015far\u0131n\u0131n anahtar\u0131d\u0131r. Modellerinizin ger\u00e7ek d\u00fcnya senaryolar\u0131nda tutarl\u0131 ve d\u00fczg\u00fcn bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar. Sentetik veri kullan\u0131m\u0131, bu s\u00fcre\u00e7te dalga yaratan son teknoloji bir stratejidir. Yapay olarak olu\u015fturulan verilerin YZ modellerinizi analiz etmenize ve ince ayar yapman\u0131za nas\u0131l yard\u0131mc\u0131 olabilece\u011fini inceleyelim.   <\/p>\n\n<ul>\n<li><strong>Performans De\u011ferlendirmesi: <\/strong>Yapay zeka modelinizi \u00e7e\u015fitli yapay veri senaryolar\u0131ndan ge\u00e7irerek ne kadar do\u011fru, esnek ve uygulanabilir olduklar\u0131n\u0131 g\u00f6rebilirsiniz. Bu, modelinizin karar verme s\u00fcrecindeki olas\u0131 kusurlar\u0131 veya \u00f6nyarg\u0131lar\u0131 belirlemenize yard\u0131mc\u0131 olur. <\/li>\n\n\n\n<li><strong>U\u00e7 Durum Testi: <\/strong>Sentetik verileri kullanarak, yapay zeka modellerinizi ger\u00e7ek verilerde nadir g\u00f6r\u00fclebilen ancak yine de dikkate al\u0131nmas\u0131 gereken u\u00e7 durumlara veya ayk\u0131r\u0131 de\u011ferlere kar\u015f\u0131 test edebilirsiniz.<\/li>\n\n\n\n<li><strong>Muhalif testler: <\/strong>Muhalif sald\u0131r\u0131lar\u0131 sim\u00fcle etmek i\u00e7in yapay veriler kullanarak modelinizin manip\u00fclasyona veya bilgisayar korsanl\u0131\u011f\u0131 giri\u015fimlerine kar\u015f\u0131 savunmas\u0131zl\u0131\u011f\u0131n\u0131 analiz edebilirsiniz.<\/li>\n\n\n\n<li><strong>Veri Dengesizli\u011finin Test Edilmesi: <\/strong>Dengesiz veri k\u00fcmeleri \u00f6nyarg\u0131l\u0131 modellere yol a\u00e7abilir. S\u0131n\u0131f da\u011f\u0131l\u0131mlar\u0131n\u0131 dengelemek i\u00e7in yapay verilerden yararlanabilir ve modelinizin bu ko\u015fullar alt\u0131nda ne kadar iyi \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 de\u011ferlendirebilirsiniz. <\/li>\n\n\n\n<li><strong>Anomali Tespiti: <\/strong>Yapay verileri kullanarak, yapay zeka modelinizin beklenmedik olaylar\u0131 veya hatalar\u0131 tan\u0131ma kapasitesini test edebilirsiniz.<\/li>\n\n\n\n<li><strong>S\u00fcrekli Test: <\/strong>Yapay zeka modelleriniz olgunla\u015ft\u0131k\u00e7a, de\u011fi\u015fen ko\u015fullara yan\u0131t verdiklerinden emin olmak i\u00e7in s\u00fcrekli inceleme ve yeniden e\u011fitim i\u00e7in sentetik verileri kullanabilirsiniz.<\/li>\n<\/ul>\n\n<p>Yapay zeka modeli, bir algoritma ve veriden \u00e7ok daha fazlas\u0131d\u0131r. Teknolojinin insan becerilerini art\u0131rd\u0131\u011f\u0131, karma\u015f\u0131k sorunlar\u0131 \u00e7\u00f6zd\u00fc\u011f\u00fc ve yeni f\u0131rsatlar yaratt\u0131\u011f\u0131 bir gelece\u011fe giden yolu g\u00f6steriyor. Karma\u015f\u0131k sorunlar\u0131n \u00fcstesinden gelmemizi, tahminlerde bulunmam\u0131z\u0131 ve i\u015flemleri \u015fa\u015f\u0131rt\u0131c\u0131 bir do\u011frulukla otomatikle\u015ftirmemizi sa\u011flar.  <\/p>\n\n<h2 class=\"wp-block-heading\">QuestionPro ile Yapay Zeka Modellerini Geli\u015ftirme<\/h2>\n\n<p>Yapay zeka modellerini geli\u015ftirmek s\u00f6z konusu oldu\u011funda, QuestionPro Survey yaz\u0131l\u0131m\u0131 harika bir m\u00fcttefik olabilir. A\u015fa\u011f\u0131daki \u015fekillerde yard\u0131mc\u0131 olabilir: <\/p>\n\n<ul>\n<li><strong>Veri Toplama: <\/strong>Yapay zeka algoritmalar\u0131 \u00f6\u011frenmek ve tahmin yapmak i\u00e7in veriye ihtiya\u00e7 duyar. QuestionPro, anketler olu\u015fturman\u0131za ve yan\u0131tlayanlardan yararl\u0131 veriler toplaman\u0131za olanak tan\u0131r. Bu veriler yapay zeka modelinizi e\u011fitmek ve geli\u015ftirmek i\u00e7in kullan\u0131labilir.  <\/li>\n\n\n\n<li><strong>\u00d6zellik M\u00fchendisli\u011fi: <\/strong>Yaz\u0131l\u0131m, \u00e7e\u015fitli soru t\u00fcrlerini i\u00e7eren anketler olu\u015fturmak i\u00e7in <a href=\"https:\/\/www.questionpro.com\/blog\/synthetic-data-generation-tools\/\">sentetik veri olu\u015fturma ara\u00e7lar\u0131<\/a> i\u00e7erir. Bu uyarlanabilirlik, yapay zeka modelinizde \u00f6zellik olarak kullan\u0131labilecek \u00e7e\u015fitli veri noktalar\u0131n\u0131 toplamak i\u00e7in gereklidir. \u00d6zellikler ne kadar \u00f6nemli ve \u00e7e\u015fitliyse modelinizin performans\u0131 da o kadar iyi olur.  <\/li>\n\n\n\n<li> <strong>Veri \u00d6n \u0130\u015fleme: <\/strong>QuestionPro, anket verilerinin temizlenmesine ve \u00f6n i\u015flenmesine yard\u0131mc\u0131 olur. Temiz ve iyi yap\u0131land\u0131r\u0131lm\u0131\u015f veriler, do\u011fru yapay zeka modellerinin e\u011fitilmesi i\u00e7in hayati \u00f6nem ta\u015f\u0131r. Yaz\u0131l\u0131m, ayk\u0131r\u0131 de\u011ferlerin kald\u0131r\u0131lmas\u0131na, eksik say\u0131lar\u0131n ele al\u0131nmas\u0131na ve veri formatlar\u0131n\u0131n standartla\u015ft\u0131r\u0131lmas\u0131na yard\u0131mc\u0131 olabilir.  <\/li>\n\n\n\n<li><strong>Duygu Analizi: <\/strong>\u0130nsan duygular\u0131n\u0131 ve hislerini anlamak genellikle yapay zeka algoritmalar\u0131 i\u00e7in faydal\u0131d\u0131r. QuestionPro, anket yan\u0131tlar\u0131ndan duygusal g\u00f6stergeleri \u00e7\u0131karmak i\u00e7in duygu analizi teknikleri sa\u011flar. Bu duygu verileri, yapay zeka modelinizin insan davran\u0131\u015f\u0131n\u0131 daha iyi anlamas\u0131na yard\u0131mc\u0131 olmak i\u00e7in kullan\u0131labilir.  <\/li>\n\n\n\n<li><strong>Geri Bildirim D\u00f6ng\u00fcs\u00fc: <\/strong>Geri bildirim d\u00f6ng\u00fcs\u00fc, yapay zeka modeli geli\u015ftirme i\u00e7in \u00e7ok \u00f6nemlidir. QuestionPro, anketler arac\u0131l\u0131\u011f\u0131yla kullan\u0131c\u0131 geri bildirimi toplayarak bir geri bildirim d\u00f6ng\u00fcs\u00fcn\u00fc kolayla\u015ft\u0131r\u0131r. Bu geri bildirim, kullan\u0131c\u0131lar\u0131n\u0131z\u0131n ihtiya\u00e7lar\u0131n\u0131 daha iyi kar\u015f\u0131lamak i\u00e7in yapay zeka modelinize ince ayar yapmak ve g\u00fcncellemek i\u00e7in kullan\u0131labilir.  <\/li>\n\n\n\n<li><strong>Otomasyon:<\/strong> QuestionPro, otomatik anket da\u011f\u0131t\u0131m\u0131 ve veri toplama olana\u011f\u0131 sunarak zamandan ve emekten tasarruf sa\u011flar. Anketlerden model e\u011fitimine sorunsuz bir veri ak\u0131\u015f\u0131 sa\u011flamak i\u00e7in yapay zeka modeli boru hatlar\u0131yla birlikte \u00e7al\u0131\u015fabilir. <\/li>\n\n\n\n<li><strong>Segmentasyon: <\/strong>Yaz\u0131l\u0131m, anket kat\u0131l\u0131mc\u0131lar\u0131n\u0131 \u00e7e\u015fitli \u00f6zelliklere g\u00f6re kategorize etmenize olanak tan\u0131r. Bu segmentasyon, farkl\u0131 kullan\u0131c\u0131 gruplar\u0131 i\u00e7in yapay zeka modeli tahminlerini veya \u00f6nerilerini ki\u015fiselle\u015ftirmek i\u00e7in kullan\u0131labilir. <\/li>\n\n\n\n<li><strong>Analizler: <\/strong>QuestionPro g\u00fc\u00e7l\u00fc analitik ve raporlama se\u00e7enekleri i\u00e7erir. Anket verilerine uyguland\u0131\u011f\u0131nda, bu bilgiler AI modelinizin performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in kullan\u0131labilir. Bu, iyile\u015ftirme alanlar\u0131n\u0131n belirlenmesine yard\u0131mc\u0131 olur.  <\/li>\n<\/ul>\n\n<p>Ara\u015ft\u0131rmalar\u0131n\u0131z\u0131 g\u00fc\u00e7lendirmeye ve veriye dayal\u0131 kararlar almaya haz\u0131r m\u0131s\u0131n\u0131z? Daha ak\u0131ll\u0131 veri toplama, analiz ve eylem i\u00e7in \u015fimdi ba\u015flay\u0131n. <\/p>\n\n<p><\/p><p style=\"text-align: center;\"><a href=\"https:\/\/www.questionpro.com\/research-edition-survey-software\/\" target=\"_blank\" rel=\"noopener\">\n  <button>DAHA FAZLA BI\u0307LGI\u0307 EDI\u0307NI\u0307N<\/button>\n<\/a>       <a href=\"https:\/\/www.questionpro.com\/a\/showEntry.do?classID=1053&amp;sourceRef=blog\" target=\"_blank\" rel=\"noopener\">\n  <button>\u00dcCRETS\u0130Z DENEME<\/button>\n<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>S\u00fcrekli de\u011fi\u015fen teknoloji d\u00fcnyas\u0131nda, yapay zeka modellerinin kullan\u0131m\u0131 giderek daha yayg\u0131n hale geliyor. Bir veri bilimcisi olarak ne kadar deneyimli [&hellip;]<\/p>\n","protected":false},"author":80,"featured_media":814311,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[1114],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Yapay Zeka Modeli: Nedir, T\u00fcrleri + Sentetik Verinin Rol\u00fc<\/title>\n<meta name=\"description\" content=\"Derin \u00f6\u011frenmeden pratik uygulamalara kadar YZ modeli d\u00fcnyas\u0131n\u0131 ke\u015ffedin. 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