{"id":477793,"date":"2023-08-09T09:20:26","date_gmt":"2023-08-09T09:20:26","guid":{"rendered":""},"modified":"2023-09-05T11:15:25","modified_gmt":"2023-09-05T11:15:25","slug":"label-smoothing","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/label-smoothing\/","title":{"rendered":"Etiket yumu\u015fatma"},"content":{"rendered":"

Etiket yumu\u015fatma, makine \u00f6\u011frenimi ve derin \u00f6\u011frenme modellerinde yayg\u0131n olarak kullan\u0131lan bir d\u00fczenleme tekni\u011fidir. E\u011fitim s\u00fcreci s\u0131ras\u0131nda hedef etiketlere az miktarda belirsizlik eklenmesini i\u00e7erir, bu da a\u015f\u0131r\u0131 uyumun \u00f6nlenmesine yard\u0131mc\u0131 olur ve modelin genelleme yetene\u011fini geli\u015ftirir. Etiket yumu\u015fatma, daha ger\u00e7ek\u00e7i bir etiket da\u011f\u0131t\u0131m\u0131 bi\u00e7imi sunarak, modelin bireysel etiketlerin kesinli\u011fine daha az ba\u011f\u0131ml\u0131 olmas\u0131n\u0131 sa\u011flayarak, g\u00f6r\u00fcnmeyen veriler \u00fczerinde performans\u0131n artmas\u0131n\u0131 sa\u011flar.<\/p>\n

Etiket yumu\u015fatman\u0131n k\u00f6keninin tarihi ve ilk s\u00f6z\u00fc<\/h2>\n

Etiket yumu\u015fatma ilk olarak Christian Szegedy ve di\u011ferleri taraf\u0131ndan 2016 y\u0131l\u0131nda yay\u0131nlanan "Bilgisayarl\u0131 G\u00f6rme i\u00e7in Ba\u015flang\u0131\u00e7 Mimarisini Yeniden D\u00fc\u015f\u00fcnmek" ba\u015fl\u0131kl\u0131 ara\u015ft\u0131rma makalesinde tan\u0131t\u0131ld\u0131. Yazarlar, etiket yumu\u015fatmay\u0131 derin evri\u015fimli sinir a\u011flar\u0131n\u0131 (CNN'ler) d\u00fczenlemek ve etkileri azaltmak i\u00e7in bir teknik olarak \u00f6nerdiler. \u00d6zellikle b\u00fcy\u00fck \u00f6l\u00e7ekli g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma g\u00f6revleri ba\u011flam\u0131nda a\u015f\u0131r\u0131 uyumun olumsuz etkileri.<\/p>\n

Etiket yumu\u015fatma hakk\u0131nda detayl\u0131 bilgi. Konuyu geni\u015fletme Etiket yumu\u015fatma.<\/h2>\n

Geleneksel denetimli \u00f6\u011frenmede model, tahmin edilen ve do\u011fru etiketler aras\u0131ndaki \u00e7apraz entropi kayb\u0131n\u0131 en aza indirmeyi hedefleyerek mutlak kesinlikte tahmin yapacak \u015fekilde e\u011fitilir. Bununla birlikte, bu yakla\u015f\u0131m, modelin yanl\u0131\u015f tahminler konusunda a\u015f\u0131r\u0131 derecede kendinden emin hale geldi\u011fi ve sonu\u00e7ta g\u00f6r\u00fcnmeyen veriler \u00fczerinde genelleme yetene\u011fini engelledi\u011fi a\u015f\u0131r\u0131 g\u00fcvenli tahminlere yol a\u00e7abilir.<\/p>\n

Etiket yumu\u015fatma, e\u011fitim s\u0131ras\u0131nda bir ge\u00e7ici etiketleme bi\u00e7imi sunarak bu sorunu giderir. Hedef olarak tek s\u0131cak kodlanm\u0131\u015f bir vekt\u00f6r (ger\u00e7ek etiket i\u00e7in bir ve di\u011ferleri i\u00e7in s\u0131f\u0131r olmak \u00fczere) atamak yerine, etiket yumu\u015fatma olas\u0131l\u0131k k\u00fctlesini t\u00fcm s\u0131n\u0131flar aras\u0131nda da\u011f\u0131t\u0131r. Do\u011fru etikete birden biraz daha d\u00fc\u015f\u00fck bir olas\u0131l\u0131k atan\u0131r ve geri kalan olas\u0131l\u0131klar di\u011fer s\u0131n\u0131flara b\u00f6l\u00fcn\u00fcr. Bu, e\u011fitim s\u00fcrecine bir belirsizlik duygusu getirerek modeli a\u015f\u0131r\u0131 uyumdan daha az e\u011filimli ve daha sa\u011flam hale getirir.<\/p>\n

Etiket yumu\u015fatman\u0131n i\u00e7 yap\u0131s\u0131. Etiket yumu\u015fatma nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h2>\n

Etiket yumu\u015fatman\u0131n dahili i\u015fleyi\u015fi birka\u00e7 ad\u0131mda \u00f6zetlenebilir:<\/p>\n

    \n
  1. \n

    Tek Kullan\u0131ml\u0131k Kodlama:<\/strong> Geleneksel denetimli \u00f6\u011frenmede, her numunenin hedef etiketi, ger\u00e7ek s\u0131n\u0131f\u0131n 1 de\u011ferini ald\u0131\u011f\u0131 ve di\u011fer t\u00fcm s\u0131n\u0131flar\u0131n 0 de\u011ferini ald\u0131\u011f\u0131 tek s\u0131cak kodlanm\u0131\u015f bir vekt\u00f6r olarak temsil edilir.<\/p>\n<\/li>\n

  2. \n

    Etiketlerin Yumu\u015fat\u0131lmas\u0131:<\/strong> Etiket yumu\u015fatma, olas\u0131l\u0131k k\u00fctlesini t\u00fcm s\u0131n\u0131flar aras\u0131nda da\u011f\u0131tarak tek s\u0131cak kodlanm\u0131\u015f hedef etiketini de\u011fi\u015ftirir. Ger\u00e7ek s\u0131n\u0131fa 1 de\u011feri atamak yerine, (1 \u2013 \u03b5) de\u011ferini atar; burada \u03b5 k\u00fc\u00e7\u00fck bir pozitif sabittir.<\/p>\n<\/li>\n

  3. \n

    Belirsizli\u011fin Da\u011f\u0131t\u0131lmas\u0131:<\/strong> Geriye kalan olas\u0131l\u0131k (\u03b5) di\u011fer s\u0131n\u0131flara b\u00f6l\u00fcnerek modelin bu s\u0131n\u0131flar\u0131n do\u011fru s\u0131n\u0131flar olma olas\u0131l\u0131\u011f\u0131n\u0131 dikkate almas\u0131 sa\u011flan\u0131r. Bu, modelin tahminleri konusunda daha az kesin olmas\u0131n\u0131 te\u015fvik ederek bir d\u00fczeyde belirsizlik ortaya \u00e7\u0131kar\u0131r.<\/p>\n<\/li>\n

  4. \n

    Kay\u0131p Hesaplamas\u0131:<\/strong> E\u011fitim s\u0131ras\u0131nda model, tahmin edilen olas\u0131l\u0131klar ile yumu\u015fat\u0131lm\u0131\u015f hedef etiketleri aras\u0131ndaki \u00e7apraz entropi kayb\u0131n\u0131 optimize eder. Kayb\u0131 yumu\u015fatan etiket, kendine a\u015f\u0131r\u0131 g\u00fcvenen tahminleri cezaland\u0131r\u0131r ve daha kalibre edilmi\u015f tahminleri te\u015fvik eder.<\/p>\n<\/li>\n<\/ol>\n

    Etiket yumu\u015fatman\u0131n temel \u00f6zelliklerinin analizi.<\/h2>\n

    Etiket yumu\u015fatman\u0131n temel \u00f6zellikleri \u015funlar\u0131 i\u00e7erir:<\/p>\n

      \n
    1. \n

      D\u00fczenleme:<\/strong> Etiket yumu\u015fatma, a\u015f\u0131r\u0131 uyumu \u00f6nleyen ve model genellemesini geli\u015ftiren bir d\u00fczenleme tekni\u011fi olarak hizmet eder.<\/p>\n<\/li>\n

    2. \n

      Kalibre edilmi\u015f Tahminler:<\/strong> Etiket yumu\u015fatma, hedef etiketlerine belirsizlik getirerek modelin daha kalibre edilmi\u015f ve daha az g\u00fcvenilir tahminler \u00fcretmesini te\u015fvik eder.<\/p>\n<\/li>\n

    3. \n

      Geli\u015ftirilmi\u015f Sa\u011flaml\u0131k:<\/strong> Etiket yumu\u015fatma, modelin belirli e\u011fitim \u00f6rneklerini ezberlemek yerine verilerdeki anlaml\u0131 kal\u0131plar\u0131 \u00f6\u011frenmeye odaklanmas\u0131na yard\u0131mc\u0131 olarak sa\u011flaml\u0131\u011f\u0131n artmas\u0131na yol a\u00e7ar.<\/p>\n<\/li>\n

    4. \n

      G\u00fcr\u00fclt\u00fcl\u00fc Etiketlerin Kullan\u0131m\u0131:<\/strong> Etiket yumu\u015fatma, g\u00fcr\u00fclt\u00fcl\u00fc veya yanl\u0131\u015f etiketleri, geleneksel tek s\u0131cak kodlanm\u0131\u015f hedeflerden daha etkili bir \u015fekilde i\u015fleyebilir.<\/p>\n<\/li>\n<\/ol>\n

      Etiket yumu\u015fatma t\u00fcrleri<\/h2>\n

      \u0130ki yayg\u0131n etiket yumu\u015fatma t\u00fcr\u00fc vard\u0131r:<\/p>\n

        \n
      1. \n

        Sabit Etiket D\u00fczeltme:<\/strong> Bu yakla\u015f\u0131mda \u03b5 de\u011feri (ger\u00e7ek etiketi yumu\u015fatmak i\u00e7in kullan\u0131lan sabit) e\u011fitim s\u00fcreci boyunca sabitlenir. Veri k\u00fcmesindeki t\u00fcm \u00f6rnekler i\u00e7in sabit kal\u0131r.<\/p>\n<\/li>\n

      2. \n

        Tavlama Etiketi Yumu\u015fatma:<\/strong> Sabit etiket yumu\u015fatman\u0131n aksine, \u03b5 de\u011feri e\u011fitim s\u0131ras\u0131nda tavlan\u0131r veya azal\u0131r. Daha y\u00fcksek bir de\u011ferle ba\u015flar ve e\u011fitim ilerledik\u00e7e yava\u015f yava\u015f azal\u0131r. Bu, modelin daha y\u00fcksek bir belirsizlik d\u00fczeyiyle ba\u015flamas\u0131na ve bunu zaman i\u00e7inde azaltarak tahminlerin kalibrasyonunda etkili bir \u015fekilde ince ayar yapmas\u0131na olanak tan\u0131r.<\/p>\n<\/li>\n<\/ol>\n

        Bu t\u00fcrler aras\u0131ndaki se\u00e7im, belirli g\u00f6reve ve veri k\u00fcmesi \u00f6zelliklerine ba\u011fl\u0131d\u0131r. Sabit etiket yumu\u015fatman\u0131n uygulanmas\u0131 daha basittir; tavlama etiket yumu\u015fatma ise optimum performans\u0131 elde etmek i\u00e7in hiper parametrelerin ayarlanmas\u0131n\u0131 gerektirebilir.<\/p>\n

        A\u015fa\u011f\u0131da iki t\u00fcr etiket yumu\u015fatman\u0131n kar\u015f\u0131la\u015ft\u0131rmas\u0131 verilmi\u015ftir:<\/p>\n\n\n\n\n\n\n\n\n
        Bak\u0131\u015f a\u00e7\u0131s\u0131<\/th>\nSabit Etiket D\u00fczeltme<\/th>\nTavlama Etiket D\u00fczeltme<\/th>\n<\/tr>\n<\/thead>\n
        \u03b5 de\u011feri<\/td>\nBoyunca sabit<\/td>\nTavlanm\u0131\u015f veya \u00e7\u00fcr\u00fcm\u00fc\u015f<\/td>\n<\/tr>\n
        Karma\u015f\u0131kl\u0131k<\/td>\nUygulamas\u0131 daha basit<\/td>\nHiperparametre ayarlamas\u0131 gerektirebilir<\/td>\n<\/tr>\n
        Kalibrasyon<\/td>\nDaha az ince ayarl\u0131<\/td>\nZamanla yava\u015f yava\u015f geli\u015fti<\/td>\n<\/tr>\n
        Verim<\/td>\nKararl\u0131 performans<\/td>\nDaha iyi sonu\u00e7lar elde etme potansiyeli<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Kullan\u0131m yollar\u0131 Etiket yumu\u015fatma, kullan\u0131ma ili\u015fkin sorunlar ve \u00e7\u00f6z\u00fcmleri.<\/h2>\n

        Etiket Yumu\u015fatmay\u0131 Kullanma<\/h3>\n

        Etiket yumu\u015fatma, sinir a\u011flar\u0131 ve derin \u00f6\u011frenme mimarileri de dahil olmak \u00fczere \u00e7e\u015fitli makine \u00f6\u011frenimi modellerinin e\u011fitim s\u00fcrecine kolayl\u0131kla dahil edilebilir. Her e\u011fitim yinelemesi s\u0131ras\u0131nda kayb\u0131 hesaplamadan \u00f6nce hedef etiketlerin de\u011fi\u015ftirilmesini i\u00e7erir.<\/p>\n

        Uygulama ad\u0131mlar\u0131 a\u015fa\u011f\u0131daki gibidir:<\/p>\n

          \n
        1. Veri k\u00fcmesini tek s\u0131cak kodlanm\u0131\u015f hedef etiketlerle haz\u0131rlay\u0131n.<\/li>\n
        2. Deney veya alan uzmanl\u0131\u011f\u0131na dayal\u0131 olarak etiket yumu\u015fatma de\u011ferini (\u03b5) tan\u0131mlay\u0131n.<\/li>\n
        3. Olas\u0131l\u0131k k\u00fctlesini daha \u00f6nce a\u00e7\u0131kland\u0131\u011f\u0131 gibi da\u011f\u0131tarak tek s\u0131cak kodlanm\u0131\u015f etiketleri yumu\u015fat\u0131lm\u0131\u015f etiketlere d\u00f6n\u00fc\u015ft\u00fcr\u00fcn.<\/li>\n
        4. Modeli yumu\u015fat\u0131lm\u0131\u015f etiketleri kullanarak e\u011fitin ve e\u011fitim s\u00fcreci s\u0131ras\u0131nda \u00e7apraz entropi kayb\u0131n\u0131 optimize edin.<\/li>\n<\/ol>\n

          Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h3>\n

          Etiket yumu\u015fatma \u00e7e\u015fitli avantajlar sunsa da baz\u0131 zorluklar\u0131 da beraberinde getirebilir:<\/p>\n

            \n
          1. \n

            Do\u011fruluk \u00dczerindeki Etki:<\/strong> Baz\u0131 durumlarda etiket yumu\u015fatma, belirsizli\u011fin ortaya \u00e7\u0131kmas\u0131 nedeniyle e\u011fitim seti \u00fczerindeki modelin do\u011frulu\u011funu bir miktar azaltabilir. Ancak genellikle etiket yumu\u015fatman\u0131n temel amac\u0131 olan test seti veya g\u00f6r\u00fcnmeyen veriler \u00fczerindeki performans\u0131 art\u0131r\u0131r.<\/p>\n<\/li>\n

          2. \n

            Hiperparametre Ayar\u0131:<\/strong> Etkili etiket yumu\u015fatma i\u00e7in \u03b5 i\u00e7in uygun bir de\u011ferin se\u00e7ilmesi \u00f6nemlidir. \u00c7ok y\u00fcksek veya \u00e7ok d\u00fc\u015f\u00fck bir de\u011fer, modelin performans\u0131n\u0131 olumsuz etkileyebilir. Optimum \u03b5 de\u011ferini bulmak i\u00e7in \u0131zgara aramas\u0131 veya rastgele arama gibi hiperparametre ayarlama teknikleri kullan\u0131labilir.<\/p>\n<\/li>\n

          3. \n

            Kay\u0131p Fonksiyonu De\u011fi\u015fikli\u011fi:<\/strong> Etiket yumu\u015fatman\u0131n uygulanmas\u0131, e\u011fitim s\u00fcrecinde kay\u0131p fonksiyonunun de\u011fi\u015ftirilmesini gerektirir. Bu de\u011fi\u015fiklik, e\u011fitim s\u00fcrecini karma\u015f\u0131kla\u015ft\u0131rabilir ve mevcut kod tabanlar\u0131nda ayarlamalar yap\u0131lmas\u0131n\u0131 gerektirebilir.<\/p>\n<\/li>\n<\/ol>\n

            Bu sorunlar\u0131 hafifletmek i\u00e7in ara\u015ft\u0131rmac\u0131lar ve uygulay\u0131c\u0131lar farkl\u0131 \u03b5 de\u011ferleriyle deneyler yapabilir, modelin do\u011frulama verileri \u00fczerindeki performans\u0131n\u0131 izleyebilir ve hiperparametrelerde buna g\u00f6re ince ayar yapabilir. Ek olarak, etiket yumu\u015fatman\u0131n belirli g\u00f6revler ve veri k\u00fcmeleri \u00fczerindeki etkisini de\u011ferlendirmek i\u00e7in kapsaml\u0131 test ve denemeler hayati \u00f6nem ta\u015f\u0131maktad\u0131r.<\/p>\n

            Ana \u00f6zellikler ve benzer terimlerle di\u011fer kar\u015f\u0131la\u015ft\u0131rmalar tablo ve liste \u015feklinde.<\/h2>\n

            A\u015fa\u011f\u0131da etiket yumu\u015fatman\u0131n di\u011fer ilgili d\u00fczenleme teknikleriyle kar\u015f\u0131la\u015ft\u0131rmas\u0131 bulunmaktad\u0131r:<\/p>\n\n\n\n\n\n\n\n\n
            D\u00fczenlile\u015ftirme Tekni\u011fi<\/th>\n\u00d6zellikler<\/th>\n<\/tr>\n<\/thead>\n
            L1 ve L2 D\u00fczenlemesi<\/td>\nA\u015f\u0131r\u0131 uyumu \u00f6nlemek i\u00e7in modeldeki b\u00fcy\u00fck a\u011f\u0131rl\u0131klar\u0131 cezaland\u0131r\u0131n.<\/td>\n<\/tr>\n
            B\u0131rakmak<\/td>\nA\u015f\u0131r\u0131 uyumu \u00f6nlemek i\u00e7in e\u011fitim s\u0131ras\u0131nda n\u00f6ronlar\u0131 rastgele devre d\u0131\u015f\u0131 b\u0131rak\u0131n.<\/td>\n<\/tr>\n
            Veri Artt\u0131rma<\/td>\nVeri k\u00fcmesi boyutunu art\u0131rmak i\u00e7in e\u011fitim verilerinin \u00e7e\u015fitlerini tan\u0131t\u0131n.<\/td>\n<\/tr>\n
            Etiket P\u00fcr\u00fczs\u00fczle\u015ftirme<\/td>\nKalibre edilmi\u015f tahminleri te\u015fvik etmek i\u00e7in hedef etiketlerini yumu\u015fat\u0131n.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            T\u00fcm bu teknikler model genellemesini geli\u015ftirmeyi ama\u00e7larken, etiket yumu\u015fatma hedef etiketlerde belirsizlik yaratmaya odaklanmas\u0131yla \u00f6ne \u00e7\u0131k\u0131yor. Modelin daha g\u00fcvenli ancak temkinli tahminler yapmas\u0131na yard\u0131mc\u0131 olur, bu da g\u00f6r\u00fcnmeyen veriler \u00fczerinde daha iyi performansa yol a\u00e7ar.<\/p>\n

            Etiket yumu\u015fatmayla ilgili gelece\u011fin perspektifleri ve teknolojileri.<\/h2>\n

            Etiket yumu\u015fatma gibi d\u00fczenleme teknikleri de dahil olmak \u00fczere derin \u00f6\u011frenme ve makine \u00f6\u011frenimi alan\u0131 s\u00fcrekli olarak geli\u015fmektedir. Ara\u015ft\u0131rmac\u0131lar, model performans\u0131n\u0131 ve genellemeyi daha da geli\u015ftirmek i\u00e7in daha geli\u015fmi\u015f d\u00fczenleme y\u00f6ntemlerini ve bunlar\u0131n kombinasyonlar\u0131n\u0131 ara\u015ft\u0131r\u0131yorlar. Etiket yumu\u015fatma ve ilgili alanlarda gelecekteki ara\u015ft\u0131rmalar i\u00e7in baz\u0131 potansiyel y\u00f6nler \u015funlard\u0131r:<\/p>\n

              \n
            1. \n

              Uyarlanabilir Etiket Yumu\u015fatma:<\/strong> Modelin tahminlerine olan g\u00fcvenine dayal\u0131 olarak \u03b5 de\u011ferinin dinamik olarak ayarland\u0131\u011f\u0131 tekniklerin ara\u015ft\u0131r\u0131lmas\u0131. Bu, e\u011fitim s\u0131ras\u0131nda daha uyarlanabilir belirsizlik seviyelerine yol a\u00e7abilir.<\/p>\n<\/li>\n

            2. \n

              Etki Alan\u0131na \u00d6zel Etiket D\u00fczeltme:<\/strong> Etkinli\u011fini daha da art\u0131rmak amac\u0131yla etiket yumu\u015fatma tekniklerini belirli alanlar veya g\u00f6revler i\u00e7in uyarlamak.<\/p>\n<\/li>\n

            3. \n

              Di\u011fer D\u00fczenlile\u015ftirme Teknikleriyle Etkile\u015fim:<\/strong> Karma\u015f\u0131k modellerde daha iyi genelleme elde etmek i\u00e7in etiket yumu\u015fatma ve di\u011fer d\u00fczenleme y\u00f6ntemleri aras\u0131ndaki sinerjinin ara\u015ft\u0131r\u0131lmas\u0131.<\/p>\n<\/li>\n

            4. \n

              Takviyeli \u00d6\u011frenimde Etiket Yumu\u015fatma:<\/strong> Etiket yumu\u015fatma tekniklerinin, \u00f6d\u00fcllerdeki belirsizliklerin \u00f6nemli bir rol oynayabilece\u011fi takviyeli \u00f6\u011frenme alan\u0131na geni\u015fletilmesi.<\/p>\n<\/li>\n<\/ol>\n

              Proxy sunucular\u0131 nas\u0131l kullan\u0131labilir veya Etiket yumu\u015fatmayla nas\u0131l ili\u015fkilendirilebilir?<\/h2>\n

              Proxy sunucular\u0131 ve etiket yumu\u015fatma, teknoloji ortam\u0131nda farkl\u0131 ama\u00e7lara hizmet etti\u011finden do\u011frudan ili\u015fkili de\u011fildir. Ancak proxy sunucular, etiket yumu\u015fatmay\u0131 \u00e7e\u015fitli \u015fekillerde uygulayan makine \u00f6\u011frenimi modelleriyle birlikte kullan\u0131labilir:<\/p>\n

                \n
              1. \n

                Veri toplama:<\/strong> Proxy sunucular\u0131, farkl\u0131 co\u011frafi konumlardan \u00e7e\u015fitli veri k\u00fcmelerini toplamak i\u00e7in kullan\u0131labilir; b\u00f6ylece makine \u00f6\u011frenimi modeli i\u00e7in e\u011fitim verilerinin \u00e7e\u015fitli kullan\u0131c\u0131 pop\u00fclasyonlar\u0131n\u0131 temsil etmesi sa\u011flan\u0131r.<\/p>\n<\/li>\n

              2. \n

                Anonimlik ve Gizlilik:<\/strong> Veri toplama s\u0131ras\u0131nda kullan\u0131c\u0131 verilerini anonimle\u015ftirmek i\u00e7in proxy sunucular kullan\u0131labilir, b\u00f6ylece modeller hassas bilgiler \u00fczerinde e\u011fitilirken gizlilik endi\u015feleri giderilir.<\/p>\n<\/li>\n

              3. \n

                Model Sunumu i\u00e7in Y\u00fck Dengeleme:<\/strong> Da\u011f\u0131t\u0131m a\u015famas\u0131nda proxy sunucular, y\u00fck dengeleme ve model \u00e7\u0131kar\u0131m isteklerini makine \u00f6\u011frenimi modelinin birden \u00e7ok \u00f6rne\u011fi aras\u0131nda verimli bir \u015fekilde da\u011f\u0131tmak i\u00e7in kullan\u0131labilir.<\/p>\n<\/li>\n

              4. \n

                \u00d6nbelle\u011fe Alma Modeli Tahminleri:<\/strong> Proxy sunucular\u0131, makine \u00f6\u011frenimi modeli taraf\u0131ndan yap\u0131lan tahminleri \u00f6nbelle\u011fe alarak tekrarlanan sorgular i\u00e7in yan\u0131t s\u00fcrelerini ve sunucu y\u00fcklerini azaltabilir.<\/p>\n<\/li>\n<\/ol>\n

                Proxy sunucular\u0131 ve etiket yumu\u015fatma ba\u011f\u0131ms\u0131z olarak \u00e7al\u0131\u015f\u0131rken, ilki, g\u00fc\u00e7l\u00fc veri toplama ve etiket yumu\u015fatma teknikleri kullan\u0131larak e\u011fitilmi\u015f makine \u00f6\u011frenimi modellerinin verimli bir \u015fekilde da\u011f\u0131t\u0131lmas\u0131n\u0131n sa\u011flanmas\u0131nda destekleyici bir rol oynayabilir.<\/p>\n

                \u0130lgili Ba\u011flant\u0131lar<\/h2>\n

                Etiket yumu\u015fatma ve bunun derin \u00f6\u011frenmedeki uygulamalar\u0131 hakk\u0131nda daha fazla bilgi i\u00e7in a\u015fa\u011f\u0131daki kaynaklar\u0131 incelemeyi d\u00fc\u015f\u00fcn\u00fcn:<\/p>\n

                  \n
                1. Bilgisayarla G\u00f6rme i\u00e7in Ba\u015flang\u0131\u00e7 Mimarisini Yeniden D\u00fc\u015f\u00fcnmek<\/a> \u2013 Etiket yumu\u015fatmay\u0131 tan\u0131tan orijinal ara\u015ft\u0131rma makalesi.<\/li>\n
                2. Etiket D\u00fczeltmeye Nazik Bir Giri\u015f<\/a> \u2013 Yeni ba\u015flayanlar i\u00e7in etiket yumu\u015fatma konusunda ayr\u0131nt\u0131l\u0131 bir e\u011fitim.<\/li>\n
                3. Etiket D\u00fczeltmeyi Anlamak<\/a> \u2013 Etiket yumu\u015fatman\u0131n ve bunun model e\u011fitimi \u00fczerindeki etkilerinin kapsaml\u0131 bir a\u00e7\u0131klamas\u0131.<\/li>\n<\/ol>","protected":false},"featured_media":468749,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477793","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Label Smoothing<\/mark>","faq_items":[{"question":"What is Label Smoothing?","answer":"

                  Label smoothing is a regularization technique used in machine learning and deep learning models. It involves adding a small amount of uncertainty to the target labels during training to prevent overfitting and improve model generalization.<\/p>"},{"question":"How was Label Smoothing introduced?","answer":"

                  Label smoothing was first introduced in the research paper \"Rethinking the Inception Architecture for Computer Vision\" by Christian Szegedy et al. in 2016. The authors proposed it as a regularization method for large-scale image classification tasks.<\/p>"},{"question":"How does Label Smoothing work?","answer":"

                  Label smoothing modifies the traditional one-hot encoded target labels by distributing the probability mass among all classes. The true label is assigned a value slightly less than one, and the remaining probabilities are divided among other classes, introducing a sense of uncertainty during training.<\/p>"},{"question":"What are the types of Label Smoothing?","answer":"

                  There are two common types of label smoothing: fixed label smoothing and annealing label smoothing. Fixed label smoothing uses a constant value for uncertainty throughout training, while annealing label smoothing gradually decreases the uncertainty over time.<\/p>"},{"question":"How can I use Label Smoothing?","answer":"

                  To use label smoothing, modify the target labels before computing the loss during training. Prepare the dataset with one-hot encoded labels, choose a value for uncertainty (\u03b5), and convert the labels into softened labels with the probability distribution.<\/p>"},{"question":"What benefits does Label Smoothing offer?","answer":"

                  Label smoothing improves model robustness and calibration, making it less reliant on individual labels during prediction. It also handles noisy labels better and enhances generalization performance on unseen data.<\/p>"},{"question":"Are there any challenges with Label Smoothing?","answer":"

                  While label smoothing improves generalization, it might slightly reduce accuracy on the training set. Choosing an appropriate \u03b5 value requires experimentation, and implementation may need modification of the loss function.<\/p>"},{"question":"How can Proxy Servers be associated with Label Smoothing?","answer":"

                  Proxy servers are not directly related to label smoothing but can complement it. They can aid in diverse data collection, anonymizing user data, load balancing for model serving, and caching model predictions to optimize performance.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477793","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477793\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468749"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=477793"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}