\n| Verilerle Performans<\/td>\n | B\u00fcy\u00fck, yap\u0131land\u0131r\u0131lmam\u0131\u015f veri k\u00fcmeleriyle m\u00fckemmel uyum sa\u011flar.<\/td>\n | Verilere fazla ba\u011f\u0131ml\u0131 olmadan, temel muhakeme sa\u011flar.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Derin \u00f6\u011frenme, b\u00fcy\u00fck miktarda yap\u0131land\u0131r\u0131lmam\u0131\u015f veriyi i\u015fleme kabiliyetiyle \u00f6ne \u00e7\u0131kmaktad\u0131r ve bu da onu g\u00f6r\u00fcnt\u00fc ve ses tan\u0131ma gibi g\u00f6revler i\u00e7in ideal hale getirmektedir. Buna kar\u015f\u0131l\u0131k, geleneksel makine \u00f6\u011frenimi genellikle yorumlanabilirli\u011fin \u00f6nemli oldu\u011fu daha basit problemler i\u00e7in tercih edilir. Yapay zeka, ak\u0131ll\u0131 sistemlere ula\u015fmak i\u00e7in ara\u00e7lar olarak hem makine \u00f6\u011frenimini hem de derin \u00f6\u011frenmeyi i\u00e7eren kapsay\u0131c\u0131 bir \u00e7er\u00e7eve olmaya devam etmektedir. Her biri farkl\u0131 rollere hizmet eder ve do\u011fru yakla\u015f\u0131m\u0131n se\u00e7ilmesi, eldeki sorunun karma\u015f\u0131kl\u0131\u011f\u0131na ve do\u011fas\u0131na ba\u011fl\u0131d\u0131r.<\/p>\n Derin \u00d6\u011frenme ve Makine \u00d6\u011frenimi Farklar\u0131 Nelerdir?<\/strong><\/h2>\nA\u015fa\u011f\u0131daki tabloda derin \u00f6\u011frenme ve makine \u00f6\u011frenimi aras\u0131ndaki farklara dair genel bir bak\u0131\u015f bulabilirsiniz:<\/p>\n \n\n\n\u00d6zellikler<\/strong><\/td>\nDerin \u00d6\u011frenme<\/strong><\/td>\nMakine \u00d6\u011frenimi<\/strong><\/td>\n<\/tr>\n\n| Veri \u0130\u015fleme<\/td>\n | B\u00fcy\u00fck veri k\u00fcmeleri gerektirir.<\/td>\n | Daha k\u00fc\u00e7\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131r.<\/td>\n<\/tr>\n | \n| \u00d6zellik M\u00fchendisli\u011fi<\/td>\n | Otomatiktir, ham verilerden \u00f6\u011frenir.<\/td>\n | Manuel \u00f6zellik se\u00e7imi gerektirir.<\/td>\n<\/tr>\n | \n| Donan\u0131m Gereksinimleri<\/td>\n | Y\u00fcksek GPU\/TPU gereklidir.<\/td>\n | Orta CPU’lar yeterlidir.<\/td>\n<\/tr>\n | \n| Performans<\/td>\n | B\u00fcy\u00fck verilerle y\u00fcksek do\u011fruluk sa\u011flan\u0131r.<\/td>\n | Yap\u0131land\u0131r\u0131lm\u0131\u015f veriler i\u00e7in orta d\u00fczeyde do\u011fruluk sa\u011flan\u0131r.<\/td>\n<\/tr>\n | \n| Kullan\u0131m Durumlar\u0131<\/td>\n | G\u00f6r\u00fcnt\u00fc tan\u0131ma, NLP, robotik<\/td>\n | Regresyon, s\u0131n\u0131fland\u0131rma, k\u00fcmeleme<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Derin \u00f6\u011frenme ve makine \u00f6\u011frenmesi aras\u0131ndaki temel farklar, \u00f6\u011frenme yakla\u015f\u0131mlar\u0131 ve g\u00f6rev karma\u015f\u0131kl\u0131\u011f\u0131d\u0131r. Derin \u00f6\u011frenme, geni\u015f verilerden \u00f6\u011frenme yoluyla bilgisayarl\u0131 g\u00f6r\u00fc, konu\u015fma tan\u0131ma ve do\u011fal dil i\u015fleme gibi g\u00f6revlerde \u00fcst\u00fcnl\u00fck sa\u011flar.<\/p>\n Makine \u00f6\u011frenimi, g\u00fc\u00e7l\u00fc olmas\u0131na ra\u011fmen \u00f6zellik geli\u015ftirme i\u00e7in daha fazla insan \u00e7abas\u0131na ihtiya\u00e7 duyabilir ve daha basit verilere ve sorunlara uygundur.<\/p>\n Derin \u00f6\u011frenme ve makine \u00f6\u011frenmesi aras\u0131nda karar vermek soruna, veriye ve kaynaklara ba\u011fl\u0131d\u0131r. Derin \u00f6\u011frenme karma\u015f\u0131k, yap\u0131land\u0131r\u0131lmam\u0131\u015f verilere uygunken, geleneksel y\u00f6ntemler daha k\u00fc\u00e7\u00fck, yap\u0131land\u0131r\u0131lm\u0131\u015f veri k\u00fcmeleri i\u00e7in daha iyi \u00e7al\u0131\u015f\u0131r.<\/p>\n Derin \u00d6\u011frenme Ne Zaman Ortaya \u00c7\u0131kt\u0131?<\/strong><\/h2>\nDerin \u00f6\u011frenme, makine \u00f6\u011frenimi ve yapay zekan\u0131n tarihi, Warren McCulloch ve Walter Pits’in sinir a\u011flar\u0131 i\u00e7in e\u015fik mant\u0131\u011f\u0131 ad\u0131 verilen algoritmalara dayanan bir bilgisayar modeli olu\u015fturduklar\u0131 1940’l\u0131 y\u0131llara kadar uzanmaktad\u0131r. Bu, insan sinir a\u011flar\u0131n\u0131 bir makine ile kopyalamaya y\u00f6nelik ilk giri\u015fimdir. 1958 y\u0131l\u0131nda Frank Rosenblatt, \u00f6r\u00fcnt\u00fc tan\u0131maya odaklanan ve iki katmanl\u0131 sinir a\u011flar\u0131n\u0131 kullanan bir algoritma yaratt\u0131 ve ek katmanlar\u0131n kullan\u0131labilece\u011fini \u00f6nerdi. 1980 y\u0131l\u0131nda Kenihiko Fukishima, el yaz\u0131s\u0131 ve di\u011fer \u00f6r\u00fcnt\u00fc tan\u0131ma i\u015flemleri i\u00e7in kullan\u0131labilecek hiyerar\u015fik ve \u00e7ok katmanl\u0131 bir yapay sinir a\u011f\u0131 \u00f6nerdi. 1989’da bilim insanlar\u0131 bu derin sinir a\u011flar\u0131n\u0131 kullanan algoritmalar olu\u015fturmay\u0131 ba\u015fard\u0131lar, ancak sistemlerin e\u011fitilmesi i\u00e7in g\u00fcnler gerekiyordu ve bu da kullan\u0131m i\u00e7in pratik de\u011fildi.<\/p>\n 2000’li y\u0131llarda yapay zeka ve makine \u00f6\u011frenimi d\u00fcnyan\u0131n d\u00f6rt bir yan\u0131ndaki program ve uygulamalarda ger\u00e7ek bir \u015fekil almaya ba\u015flad\u0131ktan sonra derin \u00f6\u011frenme terimi, ara\u015ft\u0131rmalar\u0131n \u00e7ok katmanl\u0131 bir sinir a\u011f\u0131n\u0131n her seferinde tek bir katmanla nas\u0131l e\u011fitilebilece\u011fini g\u00f6stermesinin ard\u0131ndan pop\u00fclerlik kazanmaya ba\u015flad\u0131. 2009 y\u0131l\u0131nda, yeterince b\u00fcy\u00fck bir veri setiyle a\u011flar\u0131n \u00f6nceden e\u011fitilmesine gerek olmad\u0131\u011f\u0131 ve hata oranlar\u0131n\u0131n d\u00fc\u015ft\u00fc\u011f\u00fc ke\u015ffedildi. 2012 y\u0131l\u0131na gelindi\u011finde, belirli derin \u00f6\u011frenme g\u00f6revlerinde insan d\u00fczeyinde performans elde edildi ve b\u00fcy\u00fck i\u015fletmeler derin \u00f6\u011frenme yeteneklerini geli\u015ftirmek i\u00e7in milyonlarca dolar yat\u0131r\u0131m yapmaya ba\u015flad\u0131.<\/p>\n 2000’li y\u0131llardan bu yana i\u015fletmeler, m\u00fc\u015fterileri ve al\u0131\u015fkanl\u0131klar\u0131 hakk\u0131nda genellikle b\u00fcy\u00fck veri olarak adland\u0131r\u0131lan devasa miktarlarda veri toplamaya ba\u015flad\u0131. Bu miktarda veri daha \u00f6nce hi\u00e7 g\u00f6r\u00fclmemi\u015fti ve i\u015fletmelerin t\u00fcm bu bilgilerden bir \u015fey \u00f6\u011frenmesi imkans\u0131zd\u0131. Derin \u00f6\u011frenme ve yapay zeka uygulamalar\u0131, i\u015fletmelerin verilerini ayr\u0131\u015ft\u0131rabilmeleri, ayk\u0131r\u0131 de\u011ferleri bulmak i\u00e7in hesaplama yapabilmeleri, kal\u0131plar\u0131 tan\u0131mlayabilmeleri ve ba\u011flant\u0131lar kurabilmeleri, nihayetinde sorular\u0131 yan\u0131tlayabilmeleri ve b\u00fcy\u00fck verilerini sindirilebilir par\u00e7alar haline getirebilmeleri i\u00e7in \u00f6nemli bir yol haline geldi. G\u00fcn\u00fcm\u00fczde i\u015fletmeler, m\u00fc\u015fterilerinin istedi\u011fi ve ihtiya\u00e7 duydu\u011fu \u00fcr\u00fcn ve hizmetleri nas\u0131l pazarlayacaklar\u0131n\u0131 ve \u00fcreteceklerini \u00f6\u011frenerek b\u00fcy\u00fck veri ve analiziyle geli\u015fmektedir. B\u00fcy\u00fck veri olmadan i\u015fletmeler, hedef kitleleriyle nas\u0131l ba\u011flant\u0131 kuracaklar\u0131 ve onlara ihtiya\u00e7 duyduklar\u0131 hizmetleri nas\u0131l sunacaklar\u0131 konusunda bilin\u00e7siz kararlar verebilir. Yapay zeka uygulamalar\u0131 olmadan, i\u015fletmeler b\u00fcy\u00fck verilerini anlamland\u0131ramaz.<\/p>\n Derin \u00d6\u011frenme Neden \u00d6nemli?<\/strong><\/h2>\nDerin \u00f6\u011frenme 1980’lerden beri kullan\u0131lmaktad\u0131r, ancak son y\u0131llara kadar y\u00fcksek performansl\u0131 i\u015flemciler ve donan\u0131m geli\u015fmeleri hesaplama h\u0131z\u0131n\u0131 ve verimlili\u011fini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rarak b\u00fcy\u00fck ve karma\u015f\u0131k verilerin derin \u00f6\u011frenme yoluyla analiz edilmesini m\u00fcmk\u00fcn k\u0131lm\u0131\u015ft\u0131r. Bu teknoloji s\u00fcrekli olarak geli\u015fmektedir ve ara\u015ft\u0131rmac\u0131lar ve veri bilimciler, g\u00fcnl\u00fck hayat\u0131m\u0131zda g\u00f6rd\u00fc\u011f\u00fcm\u00fcz \u00e7e\u015fitli yapay zeka uygulamalar\u0131n\u0131 dolayl\u0131 olarak y\u00f6nlendirmek i\u00e7in konu\u015fma, g\u00f6r\u00fcnt\u00fc ve metin tan\u0131ma i\u00e7in derin \u00f6\u011frenme programlar\u0131n\u0131 kullanmaktad\u0131r.<\/p>\n Derin \u00f6\u011frenme, sistemlerin a\u015fa\u011f\u0131dakileri yapmas\u0131n\u0131 sa\u011flayarak yapay zeka yeteneklerini geli\u015ftirir:<\/p>\n \n- G\u00f6r\u00fcnt\u00fcleri ve Konu\u015fmay\u0131 Tan\u0131ma\u00a0<\/strong>– Y\u00fcz tan\u0131ma, sesli asistanlar ve t\u0131bbi g\u00f6r\u00fcnt\u00fclemede kullan\u0131l\u0131r.<\/li>\n
- Do\u011fal Dili \u0130\u015fleme<\/strong>\u00a0– Sohbet robotlar\u0131na, dil \u00e7evirisine ve duygu analizine g\u00fc\u00e7 verir.<\/li>\n
- Karar Verme S\u00fcrecini Otomatikle\u015ftirin\u00a0<\/strong>– Veri analizi ve tahminlerde insan m\u00fcdahalesini azalt\u0131r.<\/li>\n
- Ki\u015fiselle\u015ftirmeyi Geli\u015ftirme<\/strong>\u00a0– E-ticaret ve e\u011flence i\u00e7in \u00f6neri motorlar\u0131n\u0131 geli\u015ftirir.<\/li>\n
- Advance Autonomous Systems<\/strong>\u00a0– S\u00fcr\u00fcc\u00fcs\u00fcz ara\u00e7lar\u0131, robotlar\u0131 ve yapay zeka asistanlar\u0131n\u0131 destekler.<\/li>\n<\/ul>\n
Derin \u00d6\u011frenme Uygulamalar\u0131<\/strong><\/h2>\nDerin \u00f6\u011frenme bir\u00e7ok farkl\u0131 alanda b\u00fcy\u00fck bir ba\u015far\u0131yla kullan\u0131lmaktad\u0131r. Derin \u00f6\u011frenmenin baz\u0131 kullan\u0131m \u00f6rnekleri a\u015fa\u011f\u0131dakileri i\u00e7ermektedir:<\/p>\n \n- G\u00f6r\u00fcnt\u00fc tan\u0131ma:\u00a0<\/strong>\u00c7e\u015fitli uygulamalarda g\u00f6r\u00fcnt\u00fc tan\u0131ma i\u00e7in kullan\u0131lmaktad\u0131r. \u00d6rne\u011fin, sosyal medyadaki foto\u011fraflar\u0131 otomatik olarak etiketlemek ve s\u00fcr\u00fcc\u00fcs\u00fcz ara\u00e7lardaki nesneleri tan\u0131mlamak i\u00e7in kullan\u0131labilir.<\/li>\n
- Do\u011fal dil i\u015fleme:<\/strong>\u00a0Do\u011fal dil i\u015fleme, dili konu\u015fman\u0131n ve anlaman\u0131n bir par\u00e7as\u0131 olan gramer ve s\u00f6zdiziminin t\u00fcm n\u00fcanslar\u0131n\u0131 anlamaya yard\u0131mc\u0131 olmak i\u00e7in derin \u00f6\u011frenme modellerini kullan\u0131r. Derin a\u011flar, nas\u0131l anlayacaklar\u0131n\u0131 ve uygun yan\u0131tlar\u0131 nas\u0131l bulacaklar\u0131n\u0131 \u00f6\u011frenmek i\u00e7in dili okur ve dinler. Bu yaz\u0131l\u0131m halihaz\u0131rda milyonlarca cihazda \u00e7eviri hizmetlerinde kullan\u0131lmaktad\u0131r.<\/li>\n
- Kestirimci bak\u0131m:\u00a0<\/strong>Sens\u00f6rlerden gelen verileri analiz ederek ekipman\u0131n ne zaman onar\u0131lmas\u0131 gerekece\u011fini tahmin etmek i\u00e7in kullan\u0131l\u0131r.<\/li>\n
- Doland\u0131r\u0131c\u0131l\u0131k tespiti:\u00a0<\/strong>\u0130\u015flem ge\u00e7mi\u015fi ve kredi kart\u0131 kullan\u0131m\u0131 gibi verileri analiz ederek doland\u0131r\u0131c\u0131l\u0131k faaliyetlerini tespit etmek i\u00e7in kullan\u0131lmaktad\u0131r.<\/li>\n
- M\u00fc\u015fteri segmentasyonu:<\/strong>\u00a0\u0130\u015fletmelerin pazarlamalar\u0131n\u0131 daha iyi hedefleyebilmeleri i\u00e7in m\u00fc\u015fterileri davran\u0131\u015flar\u0131na g\u00f6re grupland\u0131rmak i\u00e7in kullan\u0131l\u0131r.<\/li>\n
- S\u00fcr\u00fcc\u00fcs\u00fcz ara\u00e7lar:\u00a0<\/strong>Derin \u00f6\u011frenme modelleri, ara\u00e7lar\u0131n her g\u00fcn yollara \u00e7\u0131kan milyonlarca senaryoya haz\u0131rl\u0131kl\u0131 olmalar\u0131na yard\u0131mc\u0131 olmak i\u00e7in s\u00fcr\u00fcc\u00fcs\u00fcz ara\u00e7 teknolojisinde kilit \u00f6neme sahiptir. Bu uygulama, ara\u00e7lar\u0131n yoldayken nas\u0131l davranacaklar\u0131n\u0131 bilmelerine ve i\u00e7inde bulunduklar\u0131 durumlardan \u00f6\u011frenmelerine yard\u0131mc\u0131 olur.<\/li>\n
- Sanal asistanlar:\u00a0<\/strong>Siri veya Google ile konu\u015ftu\u011funuzda, derin \u00f6\u011frenen bir sinir a\u011f\u0131yla konu\u015fmu\u015f olursunuz. Sanal asistan uygulaman\u0131z, ne istedi\u011finizi anlamak, sesinizi tan\u0131mak ve arad\u0131\u011f\u0131n\u0131z \u015feyi size nas\u0131l verece\u011fini bulmak i\u00e7in konu\u015fma tan\u0131ma i\u00e7in bu a\u011flar\u0131 kullan\u0131r. Etkinli\u011fini art\u0131rmaya yard\u0131mc\u0131 olmak i\u00e7in her zaman \u00f6\u011frenir ve hesaplama yapar. Bir soruya yan\u0131t, \u00e7evrimi\u00e7i al\u0131\u015fveri\u015fe ba\u011flant\u0131 ve daha fazlas\u0131 sanal asistan\u0131n\u0131zdan gelen \u00e7\u0131kt\u0131 yan\u0131tlar\u0131d\u0131r.<\/li>\n
- E\u011flence:\u00a0<\/strong>Netflix size izlemeniz i\u00e7in yeni bir video sundu\u011funda, sizi ve tercihlerinizi \u00f6\u011frenmek i\u00e7in derin a\u011flara bakar ve ard\u0131ndan size yeni film se\u00e7enekleri sunar. Derin \u00f6\u011frenme, izleyicilerin daha ki\u015fiselle\u015ftirilmi\u015f bir e\u011flence deneyimi elde etmelerini sa\u011flar. Alg\u0131lama ve \u00f6\u011frenme \u00f6zelli\u011fi size benzersiz bir deneyim sunmaya yard\u0131mc\u0131 olur.<\/li>\n
- Sosyal medya:\u00a0<\/strong>Facebook, Twitter ve di\u011fer sosyal medya platformlar\u0131 derin \u00f6\u011frenmeyi y\u00fczlerce ve binlerce \u015fekilde kullanmaktad\u0131r. Ak\u0131\u015f\u0131n\u0131z\u0131 be\u011fenece\u011finizi d\u00fc\u015f\u00fcnd\u00fckleri i\u00e7eriklerle ki\u015fiselle\u015ftirmekten, hakk\u0131n\u0131zda bildiklerine dayal\u0131 reklamlar sunmaya kadar, bu programlar size benzersiz bir deneyim sunmak i\u00e7in her zaman hakk\u0131n\u0131zda daha fazla \u015fey \u00f6\u011frenir. Bu programlar, ak\u0131c\u0131 ve basit bir deneyim ya\u015faman\u0131za yard\u0131mc\u0131 olmak i\u00e7in g\u00f6r\u00fcnt\u00fcler ve giri\u015fler i\u00e7in y\u00fcz tan\u0131ma \u00f6zelli\u011fini bile kullan\u0131r. T\u00fcm bunlar derin \u00f6\u011frenme sayesinde m\u00fcmk\u00fcn olur.<\/li>\n<\/ul>\n
Derin \u00d6\u011frenme Modelleri<\/strong><\/h2>\nDerin \u00f6\u011frenme sistemleri, belirli g\u00f6revlere ve hedeflere ula\u015fmak i\u00e7in farkl\u0131 yap\u0131 ve \u00e7er\u00e7eveleri ele al\u0131r. Tek bir a\u011f m\u00fckemmel olarak kabul edilmemekle birlikte, baz\u0131 modeller belirli g\u00f6revleri yerine getirmek i\u00e7in daha uygundur. Do\u011fru derin \u00f6\u011frenme modelini se\u00e7mek i\u00e7in bu modeller hakk\u0131nda sa\u011flam bir bilgi edinmeniz gerekir.<\/p>\n 1. Convolutional Neural Networks (CNNs) – Evri\u015fimli Sinir A\u011flar\u0131\u00a0<\/strong><\/h3>\nEvri\u015fimli sinir a\u011flar\u0131, g\u00f6r\u00fcnt\u00fcler gibi yap\u0131land\u0131r\u0131lm\u0131\u015f \u0131zgara verilerini i\u015fleyen DLM’lerdir. G\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, y\u00fcz tan\u0131ma g\u00f6revleri ve nesne tespitinde ba\u015far\u0131l\u0131d\u0131rlar. Bu model a\u015fa\u011f\u0131daki \u015fekilde \u00e7al\u0131\u015f\u0131r:<\/p>\n \n- Evri\u015fimsel Katman\u00a0<\/strong>– Bu katman, giri\u015f g\u00f6r\u00fcnt\u00fcs\u00fcne y\u00f6nelik bir dizi filtredir ve her filtre bir \u00f6zellik haritas\u0131 olu\u015fturmak i\u00e7in g\u00f6r\u00fcnt\u00fc boyunca kayar. Bu, kenarlar, desenler ve dokular gibi farkl\u0131 \u00f6zellikleri alg\u0131lar.<\/li>\n
- Birle\u015ftirme Katman\u0131\u00a0<\/strong>– Bu katman, en \u00f6nemli bilgileri korurken \u00f6zellik haritalar\u0131n\u0131n boyutlulu\u011funu azalt\u0131r. Yayg\u0131n t\u00fcrler aras\u0131nda maksimum birle\u015ftirme ve ortalama birle\u015ftirme bulunur.<\/li>\n
- Tamamen Ba\u011fl\u0131 Katman<\/strong>\u00a0– \u00c7oklu evri\u015fim ve havuzlama katmanlar\u0131ndan sonra \u00e7\u0131kt\u0131 d\u00fczle\u015ftirilir ve bir veya daha fazla tam yo\u011fun katmana beslenir. \u00c7\u0131kt\u0131 katman\u0131 nihai s\u0131n\u0131fland\u0131rmay\u0131\/tahmini yapar.<\/li>\n<\/ul>\n
2. Recurrent Neural Networks (RNNs) – Tekrarlayan Sinir A\u011flar\u0131\u00a0<\/strong><\/h3>\nRNN’ler zaman serisi veya do\u011fal dil gibi veri dizilerindeki kal\u0131plar\u0131 tan\u0131r. \u00d6nceki girdiler hakk\u0131nda bilgi yakalayan gizli bir durumu y\u00f6netirler. Bu model a\u015fa\u011f\u0131daki \u015fekilde \u00e7al\u0131\u015f\u0131r:<\/p>\n \n- Gizli Durum<\/strong>\u00a0– Gizli durum, her ad\u0131mda mevcut girdiye ve \u00f6nceki gizli duruma g\u00f6re g\u00fcncellenir. A\u011f, ge\u00e7mi\u015f girdilerin bir haf\u0131zas\u0131n\u0131 tutabilir.<\/li>\n
- \u00c7\u0131kt\u0131<\/strong>\u00a0– Gizli durum her ad\u0131mda bir \u00e7\u0131kt\u0131 \u00fcretir. A\u011f, zaman i\u00e7inde geri yay\u0131l\u0131m (BPTT) yoluyla tahmin hatas\u0131n\u0131 en aza indirmek i\u00e7in e\u011fitilir.<\/li>\n<\/ul>\n
3. Long Short-Term Memory Networks (LSTMs) – Uzun K\u0131sa D\u00f6nemli Bellek A\u011flar\u0131<\/strong><\/h3>\nBu modeller, uzun vadeli ba\u011f\u0131ml\u0131l\u0131klar\u0131 \u00f6\u011frenebilen \u00f6zel bir RNN t\u00fcr\u00fcd\u00fcr. LSTM’ler, zaman serisi tahmini ve konu\u015fma tan\u0131ma gibi g\u00f6revlerde daha etkili olmalar\u0131n\u0131 sa\u011flamak i\u00e7in uzun vadeli ba\u011f\u0131ml\u0131l\u0131k sorunundan ka\u00e7\u0131n\u0131r. Bu derin \u00f6\u011frenme modeli a\u015fa\u011f\u0131daki \u015fekilde \u00e7al\u0131\u015f\u0131r:<\/p>\n \n- H\u00fccre Durumu<\/strong>\u00a0– T\u00fcm dizi boyunca \u00e7al\u0131\u015fan ve bir\u00e7ok ad\u0131mda bilgi ta\u015f\u0131yabilen bir h\u00fccre durumuna sahiptirler.<\/li>\n
- Kap\u0131lar<\/strong>\u00a0– \u00dc\u00e7 kap\u0131 (giri\u015f, unutma ve \u00e7\u0131k\u0131\u015f) bilgi ak\u0131\u015f\u0131n\u0131 kontrol eder.<\/li>\n
- Giri\u015f Kap\u0131s\u0131<\/strong>\u00a0– Mevcut girdiden hangi bilginin h\u00fccre durumunda g\u00fcncellenmesi gerekti\u011fini g\u00f6sterir.<\/li>\n
- \u00c7\u0131k\u0131\u015f Kap\u0131s\u0131<\/strong>\u00a0– H\u00fccre durumuna g\u00f6re \u00e7\u0131k\u0131\u015f verilmesi gereken bilgileri kontrol eder.<\/li>\n<\/ul>\n
4. Generative Adversarial Networks (GANs) – \u00c7eki\u015fmeli \u00dcretken A\u011flar (GAN’lar)<\/strong><\/h3>\nGAN’lar, iki sinir a\u011f\u0131n\u0131 rekabet\u00e7i bir ortamda e\u011fiterek ger\u00e7ek\u00e7i veriler \u00fcretir. Ger\u00e7ek\u00e7i videolar, g\u00f6r\u00fcnt\u00fcler ve sesler olu\u015fturabilirler. Bu model a\u015fa\u011f\u0131daki \u015fekilde \u00e7al\u0131\u015f\u0131r:<\/p>\n \n- \u00dcrete\u00e7 A\u011f\u0131<\/strong>\u00a0– Rastgele g\u00fcr\u00fclt\u00fcden sahte veriler olu\u015fturur.<\/li>\n
- Ay\u0131r\u0131c\u0131 A\u011f\u00a0<\/strong>– Sahte ve ger\u00e7ek veriler aras\u0131nda ayr\u0131m yaparak verilerin ger\u00e7ekli\u011fini de\u011ferlendirir.<\/li>\n
- E\u011fitim S\u00fcreci\u00a0<\/strong>– Ay\u0131r\u0131c\u0131 ve \u00fcretici ayn\u0131 anda e\u011fitilir. \u00dcrete\u00e7 daha iyi sahte veriler \u00fcreterek ayr\u0131\u015ft\u0131r\u0131c\u0131y\u0131 kand\u0131rmaya \u00e7al\u0131\u015f\u0131rken, ayr\u0131\u015ft\u0131r\u0131c\u0131 da sahte verileri tespit etmede daha iyi olmaya \u00e7al\u0131\u015f\u0131r. Bu s\u00fcre\u00e7, \u00fcretecin giderek daha ger\u00e7ek\u00e7i veriler \u00fcretmesine yol a\u00e7ar.<\/li>\n<\/ul>\n
5. Otomatik kodlay\u0131c\u0131lar<\/strong><\/h3>\nOtomatik kodlay\u0131c\u0131lar, veri s\u0131k\u0131\u015ft\u0131rma, denoising ve \u00f6zellik \u00f6\u011frenme gibi g\u00f6revler i\u00e7in denetimsiz \u00f6\u011frenme modelleridir. Otomatik kodlay\u0131c\u0131lar verileri daha d\u00fc\u015f\u00fck boyutlu temsillere kodlamay\u0131 ve orijinal verilere geri \u00e7\u00f6zmeyi \u00f6\u011frenir. Bu model a\u015fa\u011f\u0131daki \u015fekilde \u00e7al\u0131\u015f\u0131r:<\/p>\n \n- Kodlay\u0131c\u0131<\/strong>\u00a0– Giri\u015f verilerini daha d\u00fc\u015f\u00fck boyutlu gizli alan g\u00f6sterimine e\u015fler.<\/li>\n
- Gizli Alan<\/strong>\u00a0– Giri\u015f verisinin s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f versiyonunu g\u00f6sterir.<\/li>\n
- Kod \u00c7\u00f6z\u00fcc\u00fc<\/strong>\u00a0– Giri\u015f verilerini gizli g\u00f6sterimden yeniden yap\u0131land\u0131r\u0131r.<\/li>\n
- E\u011fitim\u00a0<\/strong>– A\u011f, girdi ve yeniden yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131 aras\u0131ndaki fark\u0131 azalt\u0131r.<\/li>\n<\/ul>\n
Derin \u00d6\u011frenme i\u00e7in Donan\u0131m Gereksinimleri<\/strong><\/p>\nDerin \u00f6\u011frenme modellerinin e\u011fitimi karma\u015f\u0131k algoritmalar ve b\u00fcy\u00fck miktarda veri i\u00e7erdi\u011finden, derin \u00f6\u011frenme i\u00e7in donan\u0131m gereksinimleri \u00f6nemlidir.<\/p>\n Grafik i\u015flem birimleri (GPU’lar) paralel i\u015flemlerde \u00fcst\u00fcn olduklar\u0131 i\u00e7in derin \u00f6\u011frenme i\u00e7in tercih edilmektedir. Bu, \u00f6zellikle matris ve vekt\u00f6r hesaplamalar\u0131 i\u00e7in geleneksel CPU’lardan daha h\u0131zl\u0131 i\u015flem yap\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>\n GPU sunucular\u0131, derin \u00f6\u011frenme modelleri i\u00e7in e\u011fitim s\u00fcresini \u00f6nemli \u00f6l\u00e7\u00fcde azaltarak derin \u00f6\u011frenme becerilerine sahip profesyonellere avantajlar sa\u011flayabilir. Derin \u00f6\u011frenme modellerini verimli bir \u015fekilde \u00e7al\u0131\u015ft\u0131rmak i\u00e7in do\u011fru donan\u0131m\u0131 se\u00e7mek kritik \u00f6nem ta\u015f\u0131r. Dikkate al\u0131nmas\u0131 gereken fakt\u00f6rler aras\u0131nda modelin boyutu ve karma\u015f\u0131kl\u0131\u011f\u0131, veri hacmi ve beklenen e\u011fitim s\u00fcresi yer al\u0131r.<\/p>\n Bulut tabanl\u0131 GPU sunucular\u0131 esnek ve \u00f6l\u00e7eklenebilir bir \u00e7\u00f6z\u00fcm sunarak kullan\u0131c\u0131lar\u0131n fiziksel donan\u0131ma \u00f6n yat\u0131r\u0131m yapmadan y\u00fcksek performansl\u0131 bilgi i\u015flem kaynaklar\u0131na eri\u015fmesine olanak tan\u0131r.<\/p>\n Derin \u00d6\u011frenme Kullanman\u0131n Avantajlar\u0131 ve Dezavantajlar\u0131<\/strong><\/h2>\nDerin \u00f6\u011frenme bir dizi avantaj sunsa da, derin \u00f6\u011frenmeyi kullanman\u0131n avantajlar\u0131n\u0131 ve dezavantajlar\u0131n\u0131 g\u00f6z \u00f6n\u00fcnde bulundurman\u0131z \u00e7ok \u00f6nemlidir. Bu dengeli bak\u0131\u015f a\u00e7\u0131s\u0131, profesyonellerin i\u015flerinde derin \u00f6\u011frenme tekniklerini ne zaman ve nas\u0131l uygulayacaklar\u0131 konusunda bilin\u00e7li kararlar alabilmelerini sa\u011flar.<\/p>\n Derin \u00f6\u011frenme, karma\u015f\u0131k modeller i\u00e7in b\u00fcy\u00fck miktarda veriyi analiz etmek gibi insanlar i\u00e7in ger\u00e7ekle\u015ftirilmesi zor veya imkans\u0131z olan g\u00f6revleri otomatikle\u015ftirme yetene\u011fini i\u00e7erir.<\/p>\n Derin \u00f6\u011frenme modelleri ayr\u0131ca daha fazla veriye maruz kald\u0131k\u00e7a zaman i\u00e7inde geli\u015febilir ve bu da y\u00fcz tan\u0131ma ve bilgisayar g\u00f6r\u00fc\u015f\u00fc gibi g\u00f6revlerde do\u011frulu\u011fun artmas\u0131na neden olur.<\/p>\n G\u00fc\u00e7l\u00fc y\u00f6nlerine ra\u011fmen, derin \u00f6\u011frenmenin dezavantajlar\u0131 da vard\u0131r. Ba\u015fl\u0131ca dezavantajlardan biri, derin \u00f6\u011frenme modellerini e\u011fitmek i\u00e7in b\u00fcy\u00fck miktarlarda etiketli veriye ihtiya\u00e7 duyulmas\u0131d\u0131r. Ayr\u0131ca, e\u011fitim i\u00e7in gereken bilgi i\u015flem g\u00fcc\u00fc de \u00f6nemlidir, bu da enerji t\u00fcketimi nedeniyle daha y\u00fcksek maliyetlere ve potansiyel \u00e7evresel etkilere yol a\u00e7abilir.<\/p>\n Derin \u00d6\u011frenmede Kar\u015f\u0131la\u015f\u0131lan Zorluklar<\/strong><\/h2>\nDerin \u00f6\u011frenme, g\u00fcc\u00fcne ra\u011fmen baz\u0131 zorluklarla kar\u015f\u0131 kar\u015f\u0131yad\u0131r. Bu zorluklar a\u015fa\u011f\u0131dakileri i\u00e7ermektedir:<\/p>\n \n- Y\u00fcksek Hesaplama Maliyetleri\u00a0<\/strong>– Derin modelleri e\u011fitmek i\u00e7in g\u00fc\u00e7l\u00fc GPU’lar ve b\u00fcy\u00fck veri k\u00fcmeleri gerekir.<\/li>\n
- Veri Ba\u011f\u0131ml\u0131l\u0131\u011f\u0131<\/strong>\u00a0– Etkili \u00f6\u011frenme i\u00e7in \u00e7ok miktarda etiketlenmi\u015f veriye ihtiya\u00e7 duyar.<\/li>\n
- Yorumlanabilirlik Sorunlar\u0131<\/strong>\u00a0– Derin \u00f6\u011frenme modelleri s\u0131n\u0131rl\u0131 a\u00e7\u0131klanabilirli\u011fe sahip ”kara kutular” olarak i\u015flev g\u00f6r\u00fcr.<\/li>\n
- Etik Endi\u015feler\u00a0<\/strong>– Yapay zekan\u0131n \u00f6nyarg\u0131lar\u0131 ve deepfake teknolojisi etik ve g\u00fcvenlik endi\u015felerini art\u0131rmaktad\u0131r.<\/li>\n<\/ul>\n
Derin \u00d6\u011frenmede Gelecek Trendleri<\/strong><\/h2>\n\n- Yapay Zeka Eti\u011findeki Geli\u015fmeler<\/strong>\u00a0– Yapay zeka kararlar\u0131nda adalet ve \u015feffafl\u0131\u011f\u0131n art\u0131r\u0131lmas\u0131<\/li>\n
- U\u00e7 Yapay Zeka ve Ger\u00e7ek Zamanl\u0131 \u0130\u015fleme\u00a0<\/strong>– Ger\u00e7ek zamanl\u0131 uygulamalar i\u00e7in d\u00fc\u015f\u00fck g\u00fc\u00e7l\u00fc cihazlarda yapay zeka modellerinin \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131<\/li>\n
- \u00dcretken Yapay Zeka\u00a0<\/strong>– G\u00f6r\u00fcnt\u00fclerden metinlere i\u00e7erik \u00fcretiminde yapay zeka yarat\u0131c\u0131l\u0131\u011f\u0131n\u0131n geli\u015ftirilmesi<\/li>\n
- Bilimsel Ara\u015ft\u0131rmalar i\u00e7in Yapay Zeka<\/strong>\u00a0– Fizik, biyoloji ve iklim modellemesinde karma\u015f\u0131k problemleri \u00e7\u00f6zme<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"Yapay zeka, makine \u00f6\u011frenimi ve derin \u00f6\u011frenme hakk\u0131nda \u00e7ok fazla konu\u015fulsa da, bu 3 teknoloji genellikle birbirinin yerine…\n","protected":false},"author":1,"featured_media":3020,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_singular_sidebar":"","csco_page_header_type":"","csco_appearance_grid":"","csco_page_load_nextpost":"","csco_post_video_location":[],"csco_post_video_location_hash":"","csco_post_video_url":"","csco_post_video_bg_start_time":0,"csco_post_video_bg_end_time":0},"categories":[4,3],"tags":[],"yoast_head":"\nDerin \u00d6\u011frenme Nedir? Makine \u00d6\u011frenimi ve Yapay Zeka \u0130le Aras\u0131ndaki Farklar Nelerdir? - Bulutistan Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bulutistan.com\/blog\/derin-ogrenme-nedir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Derin \u00d6\u011frenme Nedir? 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Makine \u00d6\u011frenimi ve Yapay Zeka \u0130le Aras\u0131ndaki Farklar Nelerdir? - Bulutistan Blog","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/bulutistan.com\/blog\/derin-ogrenme-nedir\/","og_locale":"tr_TR","og_type":"article","og_title":"Derin \u00d6\u011frenme Nedir? Makine \u00d6\u011frenimi ve Yapay Zeka \u0130le Aras\u0131ndaki Farklar Nelerdir? - Bulutistan Blog","og_description":"Yapay zeka, makine \u00f6\u011frenimi ve derin \u00f6\u011frenme hakk\u0131nda \u00e7ok fazla konu\u015fulsa da, bu 3 teknoloji genellikle birbirinin yerine…","og_url":"https:\/\/bulutistan.com\/blog\/derin-ogrenme-nedir\/","og_site_name":"Bulutistan Blog","article_published_time":"2025-03-13T09:32:24+00:00","article_modified_time":"2025-03-13T10:59:49+00:00","og_image":[{"width":2560,"height":1707,"url":"https:\/\/bulutistan.com\/blog\/wp-content\/uploads\/2022\/03\/Derin-Ogrenme-Nedir-scaled.jpg","type":"image\/jpeg"}],"author":"Bulutistan","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Bulutistan","Tahmini okuma s\u00fcresi":"16 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/bulutistan.com\/blog\/derin-ogrenme-nedir\/","url":"https:\/\/bulutistan.com\/blog\/derin-ogrenme-nedir\/","name":"Derin \u00d6\u011frenme Nedir? 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