{"id":2403,"date":"2025-09-14T15:10:08","date_gmt":"2025-09-14T12:10:08","guid":{"rendered":"https:\/\/zekros.com\/?p=2403"},"modified":"2025-09-17T23:19:53","modified_gmt":"2025-09-17T20:19:53","slug":"makine-derin-ogrenme","status":"publish","type":"post","link":"https:\/\/zekros.com\/en\/makine-derin-ogrenme\/","title":{"rendered":"Makine \u00d6\u011frenmesi ve Derin \u00d6\u011frenme: Farklar\u0131, Kullan\u0131m Alanlar\u0131 ve Do\u011fru Metrik Se\u00e7imi"},"content":{"rendered":"

Makine \u00d6\u011frenmesi ve Derin \u00d6\u011frenme: Farklar\u0131, Kullan\u0131m Alanlar\u0131 ve Do\u011fru Metrik Se\u00e7imi<\/h1>\n\n\n\n
\"\"<\/figure>\n\n\n\n

Yapay zek\u00e2, g\u00fcn\u00fcm\u00fcz\u00fcn en \u00f6nemli teknolojik alanlar\u0131ndan biri haline geldi. Bu alan\u0131n en \u00e7ok konu\u015fulan kavramlar\u0131 ise Makine \u00d6\u011frenmesi (Machine Learning \u2013 ML)<\/strong> ve Derin \u00d6\u011frenme (Deep Learning \u2013 DL)<\/strong>.<\/p>\n\n\n\n

Peki bu iki teknoloji aras\u0131ndaki fark nedir? Hangi durumda ML, hangi durumda DL daha avantajl\u0131d\u0131r? Ve en \u00f6nemlisi, bir modelin ba\u015far\u0131s\u0131n\u0131 nas\u0131l do\u011fru \u00f6l\u00e7ebiliriz?<\/p>\n\n\n\n

Zekros M\u00fchendislik olarak, 10\u2019dan fazla \u00fclkede aktif hizmet veriyor ve 60\u2019tan fazla \u00fclkede tamamlad\u0131\u011f\u0131m\u0131z projelerle yaz\u0131l\u0131m, end\u00fcstriyel otomasyon, siber g\u00fcvenlik ve yapay zek\u00e2 alanlar\u0131nda anahtar teslim \u00e7\u00f6z\u00fcmler<\/strong> \u00fcretiyoruz. Bu blog yaz\u0131m\u0131zda sizlere, ba\u015flang\u0131\u00e7 ve orta seviye i\u00e7in temel bilgileri aktaraca\u011f\u0131z.<\/p>\n\n\n\n

Makine \u00d6\u011frenmesi (ML)<\/h2>\n\n\n\n

Makine \u00f6\u011frenmesi, bilgisayarlar\u0131n veriden \u00f6\u011frenerek tahmin veya s\u0131n\u0131fland\u0131rma yapmas\u0131n\u0131<\/strong> sa\u011flayan y\u00f6ntemler b\u00fct\u00fcn\u00fcd\u00fcr.<\/p>\n\n\n\n

    \n
  • \u00d6zellikleri:<\/strong>\n
      \n
    • Daha az veriyle \u00e7al\u0131\u015fabilir.<\/li>\n\n\n\n
    • \u0130nsan eliyle \u00f6zellik \u00e7\u0131kar\u0131m\u0131 gerekir.<\/li>\n\n\n\n
    • Hesaplama maliyeti d\u00fc\u015f\u00fckt\u00fcr.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • \u00d6rnek Kullan\u0131m Alanlar\u0131:<\/strong>\n
        \n
      • Spam e-posta filtreleme<\/li>\n\n\n\n
      • Kredi risk analizi<\/li>\n\n\n\n
      • End\u00fcstride bak\u0131m planlamas\u0131<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n

        Derin \u00d6\u011frenme (DL)<\/h2>\n\n\n\n

        Derin \u00f6\u011frenme, makine \u00f6\u011frenmesinin alt dal\u0131d\u0131r ve \u00e7ok katmanl\u0131 yapay sinir a\u011flar\u0131n\u0131<\/strong> kullan\u0131r.<\/p>\n\n\n\n

          \n
        • \u00d6zellikleri:<\/strong>\n
            \n
          • \u00c7ok b\u00fcy\u00fck veri setleriyle daha ba\u015far\u0131l\u0131d\u0131r.<\/li>\n\n\n\n
          • \u00d6zellik \u00e7\u0131kar\u0131m\u0131 otomatik yap\u0131l\u0131r.<\/li>\n\n\n\n
          • GPU\/TPU gibi g\u00fc\u00e7l\u00fc donan\u0131mlar gerektirir.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • \u00d6rnek Kullan\u0131m Alanlar\u0131:<\/strong>\n
              \n
            • G\u00f6r\u00fcnt\u00fc tan\u0131ma (kamera ile kalite kontrol, y\u00fcz tan\u0131ma)<\/li>\n\n\n\n
            • Otonom ara\u00e7lar<\/li>\n\n\n\n
            • Sesli asistanlar<\/li>\n\n\n\n
            • Chatbotlar ve otomatik \u00e7eviri sistemleri<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n

              ML ve DL Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/h2>\n\n\n\n
              \u00d6zellik<\/th>Makine \u00d6\u011frenmesi<\/th>Derin \u00d6\u011frenme<\/th><\/tr><\/thead>
              Veri \u0130htiyac\u0131<\/strong><\/td>Daha az<\/td>\u00c7ok fazla<\/td><\/tr>
              \u00d6zellik \u00c7\u0131kar\u0131m\u0131<\/strong><\/td>\u0130nsan eliyle<\/td>Otomatik<\/td><\/tr>
              \u0130\u015flem G\u00fcc\u00fc<\/strong><\/td>Orta<\/td>Y\u00fcksek<\/td><\/tr>
              Kullan\u0131m Alan\u0131<\/strong><\/td>Tablosal veriler<\/td>G\u00f6r\u00fcnt\u00fc, ses, metin<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
              \n\n\n\n

              Model Ba\u015far\u0131s\u0131: Do\u011fru Metrik Se\u00e7imi<\/h2>\n\n\n\n

              Bir modelin performans\u0131n\u0131 de\u011ferlendirirken se\u00e7ilen metrik<\/strong>, do\u011fru karar\u0131 vermek a\u00e7\u0131s\u0131ndan kritik \u00f6neme sahiptir.<\/p>\n\n\n\n

                \n
              • S\u0131n\u0131fland\u0131rma:<\/strong> Accuracy (do\u011fruluk), Precision (kesinlik), Recall (duyarl\u0131l\u0131k), F1-Score.<\/li>\n\n\n\n
              • Regresyon:<\/strong> RMSE (k\u00f6k ortalama kare hata), MAE (ortalama mutlak hata), MAPE (y\u00fczde hata).<\/li>\n\n\n\n
              • Anomali Tespiti:<\/strong> False Positive Rate (yanl\u0131\u015f alarm oran\u0131), Precision@k, Lead Time (erken uyar\u0131 s\u00fcresi).<\/li>\n<\/ul>\n\n\n\n

                \u00d6rne\u011fin end\u00fcstriyel otomasyonda yanl\u0131\u015f alarm maliyeti y\u00fcksek olabilir, bu y\u00fczden Precision<\/strong> kritik hale gelir. Siber g\u00fcvenlikte ise bir sald\u0131r\u0131y\u0131 ka\u00e7\u0131rmamak i\u00e7in Recall<\/strong> daha \u00f6nemlidir.<\/p>\n\n\n\n

                Sonu\u00e7<\/h2>\n\n\n\n

                Makine \u00d6\u011frenmesi<\/strong>, daha az veriyle tablosal problemlerde h\u0131zl\u0131 ve verimli sonu\u00e7lar verir.<\/p>\n\n\n\n

                  \n
                • Derin \u00d6\u011frenme<\/strong>, b\u00fcy\u00fck veriyle \u00e7al\u0131\u015farak daha karma\u015f\u0131k problemleri \u00e7\u00f6zebilir.<\/li>\n\n\n\n
                • Do\u011fru metrik se\u00e7imi<\/strong>, teknik ba\u015far\u0131y\u0131 i\u015f ba\u015far\u0131s\u0131na d\u00f6n\u00fc\u015ft\u00fcrmenin anahtar\u0131d\u0131r.<\/li>\n<\/ul>\n\n\n\n

                  Zekros M\u00fchendislik olarak biz, her projede i\u015f hedeflerini merkeze alarak<\/strong> en uygun yakla\u015f\u0131m\u0131 belirliyor ve uluslararas\u0131 deneyimimizle m\u00fc\u015fterilerimize g\u00fcvenilir \u00e7\u00f6z\u00fcmler sunuyoruz.<\/p>\n\n\n\n

                  Devam\u0131 Gelecek: Orta ve \u0130leri Seviye \u0130\u00e7in Yaz\u0131 Serisi<\/h2>\n\n\n\n

                  Bu yaz\u0131, serimizin ilk ad\u0131m\u0131<\/strong>. Amac\u0131m\u0131z ba\u015flang\u0131\u00e7 ve orta seviyedeki okuyucular\u0131m\u0131za temel bir yol haritas\u0131 sunmakt\u0131.<\/p>\n\n\n\n

                  Bir sonraki yaz\u0131larda<\/strong> ise:<\/p>\n\n\n\n

                    \n
                  • Derin \u00f6\u011frenme mimarileri (CNN, RNN, Transformer)<\/li>\n\n\n\n
                  • MLOps s\u00fcre\u00e7leri (model geli\u015ftirme, deploy, izleme)<\/li>\n\n\n\n
                  • End\u00fcstriyel otomasyonda ML\/DL uygulama senaryolar\u0131<\/li>\n\n\n\n
                  • Siber g\u00fcvenlikte yapay zek\u00e2 tabanl\u0131 anomali tespiti<\/li>\n<\/ul>\n\n\n\n

                    gibi konular\u0131 daha teknik detaylarla<\/strong> ele alaca\u011f\u0131z.<\/p>","protected":false},"excerpt":{"rendered":"

                    Makine \u00d6\u011frenmesi ve Derin \u00d6\u011frenme: Farklar\u0131, Kullan\u0131m Alanlar\u0131 ve Do\u011fru Metrik Se\u00e7imi Yapay zek\u00e2, g\u00fcn\u00fcm\u00fcz\u00fcn en \u00f6nemli teknolojik alanlar\u0131ndan biri haline geldi. Bu alan\u0131n en \u00e7ok konu\u015fulan kavramlar\u0131 ise Makine \u00d6\u011frenmesi (Machine Learning \u2013 ML) ve Derin \u00d6\u011frenme (Deep Learning \u2013 DL). Peki bu iki teknoloji aras\u0131ndaki fark nedir? Hangi durumda ML, hangi durumda DL daha avantajl\u0131d\u0131r? […]<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18,86],"tags":[90,91,89,96,102,111],"class_list":["post-2403","post","type-post","status-publish","format-standard","hentry","category-all-topics","category-yz","tag-derin-ogrenme","tag-makine-ogrenmesi","tag-makine-ogrenmesi-ile-derin-ogrenme-farki","tag-model-basarisi-olcme","tag-regresyon-ve-siniflandirma-metrikleri","tag-yapay-zeka-ile-otomasyon"],"yoast_head":"\nMakine \u00d6\u011frenmesi ve Derin \u00d6\u011frenme: Farklar\u0131, Kullan\u0131m Alanlar\u0131 ve Do\u011fru Metrik Se\u00e7imi - Zekros M\u00fchendislik<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesiDerin \u00f6\u011frenmeYapay zek\u00e2Makine \u00f6\u011frenmesi ile derin \u00f6\u011frenme fark\u0131ML ve DL kar\u015f\u0131la\u015ft\u0131rmas\u0131Machine Learning nedirDeep Learning nedir\u00d6zellik m\u00fchendisli\u011fi (feature engineering,)Sinir a\u011flar\u0131Regresyon ve s\u0131n\u0131fland\u0131rma metrikleriDo\u011fru metrik se\u00e7imiModel ba\u015far\u0131s\u0131 \u00f6l\u00e7me\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/zekros.com\/en\/makine-derin-ogrenme\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makine \u00d6\u011frenmesi ve Derin \u00d6\u011frenme: Farklar\u0131, Kullan\u0131m Alanlar\u0131 ve Do\u011fru Metrik Se\u00e7imi - 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