{"id":14982,"date":"2025-03-12T18:45:15","date_gmt":"2025-03-12T15:45:15","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-ajanlari-gelistirirken-yaptigim-3-buyuk-hata\/"},"modified":"2025-03-12T18:45:15","modified_gmt":"2025-03-12T15:45:15","slug":"yapay-zeka-ajanlari-gelistirirken-yaptigim-3-buyuk-hata","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-ajanlari-gelistirirken-yaptigim-3-buyuk-hata\/","title":{"rendered":"Yapay Zeka Ajanlar\u0131 Geli\u015ftirirken Yapt\u0131\u011f\u0131m 3 B\u00fcy\u00fck Hata"},"content":{"rendered":"

<\/p>\n

Yapay Zeka Ajanlar\u0131 Geli\u015ftirirken Yapt\u0131\u011f\u0131m 3 B\u00fcy\u00fck Hata<\/p>\n

Merhaba, Fatih Soysal burada. Yapay zeka alan\u0131nda uzun y\u0131llard\u0131r \u00e7al\u0131\u015f\u0131yorum ve son zamanlarda yapay zeka ajanlar\u0131 geli\u015ftirmeye yo\u011funla\u015ft\u0131m. Bu s\u00fcre\u00e7te, bir\u00e7ok zorlukla kar\u015f\u0131la\u015ft\u0131m. Bu yaz\u0131da, \u00f6zellikle s\u0131k\u0131nt\u0131 \u00e7ekti\u011fim \u00fc\u00e7 hatay\u0131, bunlar\u0131n nedenlerini ve gelecekte nas\u0131l \u00f6nleyebilece\u011fimi payla\u015fmak istiyorum. Umar\u0131m deneyimlerim size yol g\u00f6sterir.<\/p>\n

1. Ger\u00e7ek\u00e7i Olmayan Veri Beklentisi<\/h2>\n

\u0130lk b\u00fcy\u00fck hatam, yapay zeka ajan\u0131m i\u00e7in kulland\u0131\u011f\u0131m verinin m\u00fckemmel olmas\u0131n\u0131 beklemekti. Ger\u00e7ek d\u00fcnya verileri genellikle g\u00fcr\u00fclt\u00fcl\u00fcd\u00fcr, eksiktir ve tutars\u0131zd\u0131r. Ben ise, temiz, d\u00fczenli ve eksiksiz bir veri setiyle \u00e7al\u0131\u015fabilece\u011fimi varsayd\u0131m. Sonu\u00e7 olarak, ajan\u0131n performans\u0131 beklentilerimi kar\u015f\u0131lamad\u0131. Asl\u0131nda, veri temizli\u011fi ve i\u015fleme s\u00fcre\u00e7lerine daha fazla zaman ay\u0131r\u0131p daha ger\u00e7ek\u00e7i beklentilere sahip olmal\u0131yd\u0131m. Bu hatay\u0131 \u00f6nlemek i\u00e7in, verinin ger\u00e7ek\u00e7i bir de\u011ferlendirmesini yapmak ve eksiklikleri veya g\u00fcr\u00fclt\u00fcy\u00fc gidermek i\u00e7in uygun \u00f6n i\u015fleme tekniklerini kullanmak \u015fartt\u0131r. \u00d6rne\u011fin, eksik de\u011ferleri doldurmak i\u00e7in ortalama veya medyan gibi istatistiksel y\u00f6ntemleri veya daha geli\u015fmi\u015f y\u00f6ntemler kullanabiliriz. Bu konuda daha detayl\u0131 bilgi i\u00e7in bu makaleye g\u00f6z atabilirsiniz.<\/a><\/p>\n

2. A\u015f\u0131r\u0131 Karma\u015f\u0131k Model Se\u00e7imi<\/h2>\n

\u0130kinci hatam, a\u015f\u0131r\u0131 karma\u015f\u0131k bir model se\u00e7mek oldu. Daha karma\u015f\u0131k bir modelin daha iyi performans g\u00f6sterece\u011fini d\u00fc\u015f\u00fcnd\u00fcm. Ancak, bu durum her zaman do\u011fru de\u011fildir. A\u015f\u0131r\u0131 karma\u015f\u0131k modeller, a\u015f\u0131r\u0131 uyum (overfitting) sorununa yol a\u00e7abilir. Bu da, e\u011fitim verilerinde iyi performans g\u00f6stermesine ra\u011fmen, yeni veriler \u00fczerinde ba\u015far\u0131s\u0131z olmas\u0131na neden olur. Bu sorunu \u00f6nlemek i\u00e7in, basit modellerle ba\u015flamak ve kademeli olarak karma\u015f\u0131kl\u0131\u011f\u0131 art\u0131rmak daha mant\u0131kl\u0131d\u0131r. Model se\u00e7imi s\u00fcrecinde, modelin karma\u015f\u0131kl\u0131\u011f\u0131n\u0131, performans\u0131n\u0131 ve hesaplama maliyetini dikkatlice de\u011ferlendirmek gerekiyor. Ayr\u0131ca, d\u00fczenleme teknikleri (regularization) kullanarak a\u015f\u0131r\u0131 uyumu azaltabiliriz.<\/p>\n

3. Yetersiz Test ve De\u011ferlendirme<\/h2>\n

\u00dc\u00e7\u00fcnc\u00fc hatam, yapay zeka ajan\u0131m\u0131 yeterince test etmemek ve de\u011ferlendirme \u00f6l\u00e7\u00fctlerini yanl\u0131\u015f se\u00e7mek oldu. E\u011fitim verileri \u00fczerinde iyi performans g\u00f6steriyordu, ancak ger\u00e7ek d\u00fcnya senaryolar\u0131nda ba\u015far\u0131s\u0131z oluyordu. Bu durum, farkl\u0131 test setleri kullanarak ve \u00e7e\u015fitli performans \u00f6l\u00e7\u00fctlerini inceleyerek \u00f6nlenebilirdi. Ba\u015fka bir deyi\u015fle, daha kapsaml\u0131 bir test stratejisi benimsemeli ve farkl\u0131 senaryolar\u0131 sim\u00fcle etmeliydim. \u00d6rne\u011fin, karma\u015f\u0131kl\u0131\u011f\u0131 gidermek i\u00e7in \u00e7apraz do\u011frulama (cross-validation) y\u00f6ntemlerini kullanabilir ve performans\u0131 de\u011ferlendirmek i\u00e7in uygun metrikleri (do\u011fruluk, hassasiyet, duyarl\u0131l\u0131k gibi) se\u00e7ebiliriz. <\/p>\n

Bu \u00fc\u00e7 hatay\u0131 g\u00f6z \u00f6n\u00fcnde bulundurarak, gelecekteki yapay zeka ajan geli\u015ftirme projelerimde daha ba\u015far\u0131l\u0131 olaca\u011f\u0131ma inan\u0131yorum. Umar\u0131m bu deneyimler size de yard\u0131mc\u0131 olur. Daha fazla bilgi ve deneyim payla\u015f\u0131m\u0131 i\u00e7in l\u00fctfen web sitemi<\/a> ziyaret edin.<\/p>\n

#Etiketler
\nYapay Zeka, AI, Ajan, Yapay Zeka Ajan\u0131, Makine \u00d6\u011frenmesi, Derin \u00d6\u011frenme, Hata, Geli\u015ftirme, Programlama, Veri, Model, Test, De\u011ferlendirme, Fatih Soysal, Overfitting, A\u015f\u0131r\u0131 Uyum, Cross-Validation, \u00c7apraz Do\u011frulama<\/p>\n

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Yapay zeka alan\u0131nda uzun y\u0131llard\u0131r \u00e7al\u0131\u015f\u0131yorum ve son zamanlarda yapay zeka ajanlar\u0131 geli\u015ftirmeye yo\u011funla\u015ft\u0131m. Bu s\u00fcre\u00e7te, bir\u00e7ok zorlukla kar\u015f\u0131la\u015ft\u0131m. Bu yaz\u0131da, \u00f6zellikle s\u0131k\u0131nt\u0131 \u00e7ekti\u011fim \u00fc\u00e7 hatay\u0131, bunlar\u0131n nedenlerini ve gelecekte nas\u0131l \u00f6nleyebilece\u011fimi payla\u015fmak istiyorum. 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