{"id":5703,"date":"2021-07-26T14:24:00","date_gmt":"2021-07-26T11:24:00","guid":{"rendered":"https:\/\/www.niobehosting.com\/blog\/?p=5703"},"modified":"2021-07-01T17:14:05","modified_gmt":"2021-07-01T14:14:05","slug":"deep-learning-derin-ogrenme","status":"publish","type":"post","link":"https:\/\/www.niobehosting.com\/blog\/deep-learning-derin-ogrenme\/","title":{"rendered":"Deep Learning (Derin \u00d6\u011frenme) Nedir?"},"content":{"rendered":"\n

Deep learning <\/strong>yani derin \u00f6\u011frenme<\/strong>, makine \u00f6\u011freniminin bir alt k\u00fcmesi olarak d\u00fc\u015f\u00fcn\u00fclebilir. Bilgisayar algoritmalar\u0131n\u0131 inceleyerek kendi kendine \u00f6\u011frenmeyi m\u00fcmk\u00fcn k\u0131lan ve geli\u015ftirmeye dayal\u0131 bir aland\u0131r. Makine \u00f6\u011frenimi daha basit kavramlar kullan\u0131rken; derin \u00f6\u011frenme, insanlar\u0131n nas\u0131l d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fcn\u00fc ve \u00f6\u011frendi\u011fini taklit etmek i\u00e7in tasarlanm\u0131\u015f yapay sinir a\u011flar\u0131yla \u00e7al\u0131\u015f\u0131r. Yapay sinir a\u011f\u0131 modelinin en k\u00fc\u00e7\u00fck par\u00e7as\u0131 perceptron<\/strong>‘d\u0131r. Temel olarak e\u011fitilebilecek tek bir yapay sinir h\u00fccresinden olu\u015fur.<\/p>\n\n\n\n

Derin \u00f6\u011frenme, temsili \u00f6\u011frenmeye dayan\u0131r. G\u00f6reve \u00f6zg\u00fc algoritmalar\u0131 kullanmak yerine, temsili \u00f6rneklerden \u00f6\u011frenir. \u00d6rne\u011fin, kedileri t\u00fcre g\u00f6re tan\u0131yan bir model olu\u015fturmak istiyorsan\u0131z, bir\u00e7ok farkl\u0131 kedi g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc i\u00e7eren bir veri taban\u0131 haz\u0131rlaman\u0131z gerekir.<\/p>\n\n\n\n

Bug\u00fcn, bu alandaki ara\u015ft\u0131rmalar neredeyse tamamen derin \u00f6\u011frenmeye odaklanm\u0131\u015ft\u0131r. Bu da g\u00f6r\u00fcnt\u00fc ve metin i\u015flemede b\u00fcy\u00fck ilerlemelere yol a\u00e7m\u0131\u015ft\u0131r.<\/p>\n\n\n\n

Deep Learning (Derin \u00d6\u011frenme) Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n\n\n\n
\"\"<\/figure><\/div>\n\n\n\n

Derin \u00f6\u011frenme, makinelerin s\u00fcrekli b\u00fcy\u00fck veri kitapl\u0131klar\u0131ndan \u00f6\u011frenmesine olanak tan\u0131yan, yapay sinir a\u011flar\u0131 ad\u0131 verilen karma\u015f\u0131k, katmanl\u0131 algoritmalar\u0131n geli\u015ftirilmesini i\u00e7erir. Bir algoritman\u0131n derinlemesine de\u011ferlendirilebilmesi i\u00e7in genellikle en az alt\u0131 katman, (ancak \u00e7o\u011fu zaman daha fazla) gerekir. Her bir kod katman\u0131, veri k\u00fcmesinde belirli bir model bulmaktan sorumludur. \u00d6rne\u011fin, Spotify gibi bir \u015firket i\u00e7in yeni m\u00fczik \u00f6nermeyi ama\u00e7layan bir derin \u00f6\u011frenme algoritmas\u0131n\u0131n <\/strong>birka\u00e7 katman\u0131 olacakt\u0131r. Bir katman, \u00f6zellikle dinlenen \u00f6nceki m\u00fczik t\u00fcrlerine bakabilir. Ba\u015fka bir katman, ho\u015flan\u0131lan ya da ho\u015flan\u0131lmayan veya yinelenen ya da tekrarlanan \u015fark\u0131lara bakabilir.<\/p>\n\n\n\n

Veri k\u00fcmesi b\u00fcy\u00fcd\u00fck\u00e7e ve algoritmalardan yeni veriler elde edildik\u00e7e, derin \u00f6\u011frenme bu bilgileri \u00f6l\u00e7eklenebilir hale getirir. Bu algoritmalar\u0131 geli\u015ftirmek i\u00e7in en \u00e7ok kullan\u0131lan programlar dilleri:\u00a0 Python<\/a>, C\/C++, Java<\/a>, R, Javascript<\/a>\u2019tir.<\/p>\n\n\n\n

Derin \u00d6\u011frenmenin Temeli: Makine \u00d6\u011frenimi<\/h2>\n\n\n\n
\"\"<\/figure><\/div>\n\n\n\n

Klasik makine \u00f6\u011freniminin amac\u0131<\/strong>, her kural\u0131 a\u00e7\u0131k\u00e7a programlamak zorunda kalmadan, bir makineye bir sorunu nas\u0131l \u00e7\u00f6zece\u011fini \u00f6\u011frenme yetene\u011fi vermektir. Bu sayede makineler, veri tabanl\u0131 \u00f6\u011frenme yoluyla davran\u0131\u015flar\u0131 modelleyerek sorunlar\u0131 \u00e7\u00f6zer. Fakat, bir makine \u00f6\u011frenimi algoritmas\u0131 arac\u0131l\u0131\u011f\u0131yla bir problemi modellemeden \u00f6nce genellikle veriler \u00fczerinde bir dizi d\u00f6n\u00fc\u015f\u00fcm ger\u00e7ekle\u015ftirmek gerekir. Manuel olarak yap\u0131lan bu d\u00f6n\u00fc\u015f\u00fcmler, \u00e7\u00f6z\u00fclecek i\u015f problemi ve kullan\u0131lan algoritman\u0131n se\u00e7imi ile belirlenir.<\/strong> Bu veri i\u015fleme genellikle \u00e7ok zaman al\u0131r ve uzmanl\u0131k gerektirebilir. Veri d\u00f6n\u00fc\u015f\u00fcmleri, problemin makine \u00f6\u011frenimi algoritmas\u0131 taraf\u0131ndan kolayca anla\u015f\u0131labilen ve yorumlanabilen bir temsilinin in\u015fas\u0131 olarak g\u00f6r\u00fclebilir.<\/p>\n\n\n\n

Makine \u00f6\u011frenimi \u00fc\u00e7 ana \u00f6\u011frenim y\u00f6netiminden olu\u015fur: Supervised learning, unsupervised learning ve reinforcement learning<\/strong>. Supervised learning, unsupervised learning methodlar\u0131ndan tamamen farkl\u0131 olmamas\u0131na ra\u011fmen, reinforcement learning, insanlar\u0131n \u00f6\u011frenme \u015feklini taklit eder. \u00c7evresiyle etkile\u015fime girer ve bu etkile\u015fimlerin sonu\u00e7lar\u0131n\u0131 g\u00f6zlemleyerek \u00f6\u011frenir. <\/p>\n\n\n\n

Derin \u00f6\u011frenmedeki fikir ise, bu ilgili veri temsilini \u00f6\u011frenme a\u015famas\u0131 boyunca otomatik olarak olu\u015fturmak ve b\u00f6ylece insan m\u00fcdahalesinden ka\u00e7\u0131nmakt\u0131r. Buna temsil yoluyla \u00f6\u011frenme denir. Derin \u00f6\u011frenme algoritmas\u0131<\/strong>, verilerin giderek karma\u015f\u0131kla\u015fan hiyerar\u015fik temsillerini \u00f6\u011frenir. Bu algoritma bu nedenle sinyal verilerine (g\u00f6r\u00fcnt\u00fcler, metinler, sesler) uyarlanm\u0131\u015ft\u0131r.<\/p>\n\n\n\n

Makine \u00d6\u011frenimi ve Deep Learning (Derin \u00d6\u011frenme) Aras\u0131ndaki Fark Nedir?<\/h2>\n\n\n\n
\"\"<\/figure><\/div>\n\n\n\n

Makine \u00f6\u011frenimi,<\/strong> sisteme y\u00fcklenen \u00f6nceden i\u015flenmi\u015f b\u00fcy\u00fck bir veri k\u00fcmesinden yeni bilgiler \u00e7\u0131karmaya \u00e7al\u0131\u015f\u0131r. Programc\u0131lar\u0131n makinenin kurallar\u0131n\u0131 form\u00fcle etmesi gerekir ve makine bunlara g\u00f6re \u00f6\u011frenir. Hatalar\u0131 d\u00fczeltmek i\u00e7in bazen bir insan m\u00fcdahalesi gerekir.<\/p>\n\n\n\n

Ancak deep learning<\/strong> (derin \u00f6\u011frenme) biraz farkl\u0131d\u0131r. Derin \u00f6\u011frenme, bir algoritman\u0131n bir tahminin do\u011fru olup olmad\u0131\u011f\u0131n\u0131 kendi ba\u015f\u0131na belirlemesine izin veren birden fazla katman kullan\u0131r. Bir derin \u00f6\u011frenme modeli, tipik olarak, verileri mant\u0131ksal bir yap\u0131yla analiz etmek ve bunu, bir insan\u0131n nas\u0131l sonu\u00e7 \u00e7\u0131karaca\u011f\u0131na \u00e7ok benzer bir \u015fekilde yapmak i\u00e7in tasarlan\u0131r. Bu katmanl\u0131 yakla\u015f\u0131m, insan beynine \u00e7ok benzer \u015fekilde kendi kendini d\u00fczenleyen \u00f6\u011frenmede \u00e7ok daha yetenekli bir y\u00f6ntemle sonu\u00e7lan\u0131r.<\/p>\n\n\n\n

Yapay zekan\u0131n bu iki alt k\u00fcmesi aras\u0131nda bir\u00e7ok farkl\u0131l\u0131k olsa da, makine \u00f6\u011frenimi ve derin \u00f6\u011frenme aras\u0131ndaki 5 temel fark a\u015fa\u011f\u0131daki \u015fekildedir<\/p>\n\n\n\n

1. \u0130nsan M\u00fcdahalesi<\/h3>\n\n\n\n

Makine \u00f6\u011freniminden sonu\u00e7 almak i\u00e7in daha fazla insan m\u00fcdahalesi gerekir. Derin \u00f6\u011frenmenin kurulmas\u0131 daha karma\u015f\u0131kt\u0131r ancak daha sonra minimum m\u00fcdahale gerektirir.<\/p>\n\n\n\n

2. Donan\u0131m<\/h3>\n\n\n\n

Makine \u00f6\u011frenimi programlar\u0131, derin \u00f6\u011frenme algoritmalar\u0131ndan daha az karma\u015f\u0131k olma e\u011filimindedir ve genellikle geleneksel bilgisayarlarda \u00e7al\u0131\u015fabilir, ancak derin \u00f6\u011frenme sistemleri \u00e7ok daha g\u00fc\u00e7l\u00fc donan\u0131m ve kaynaklar gerektirir.<\/p>\n\n\n\n

3. Zaman<\/h3>\n\n\n\n

Makine \u00f6\u011frenimi sistemleri h\u0131zl\u0131 bir \u015fekilde kurup \u00e7al\u0131\u015ft\u0131r\u0131labilir, ancak sonu\u00e7lar\u0131n\u0131n g\u00fcc\u00fc genellikle s\u0131n\u0131rl\u0131d\u0131r. Derin \u00f6\u011frenme sistemlerinin kurulmas\u0131 ise daha fazla zaman al\u0131r, ancak an\u0131nda sonu\u00e7 \u00fcretebilir (daha fazla veri elde edildik\u00e7e kalitenin zamanla artmas\u0131 muhtemeldir).<\/p>\n\n\n\n

4. Yakla\u015f\u0131m<\/h3>\n\n\n\n

Makine \u00f6\u011frenimi, yap\u0131land\u0131r\u0131lm\u0131\u015f veri gerektirme e\u011filimindedir ve do\u011frusal regresyon gibi geleneksel algoritmalar\u0131 kullan\u0131r. Derin \u00f6\u011frenme, sinir a\u011flar\u0131n\u0131 kullan\u0131r ve b\u00fcy\u00fck hacimli yap\u0131land\u0131r\u0131lmam\u0131\u015f verileri bar\u0131nd\u0131racak \u015fekilde tasarlanm\u0131\u015ft\u0131r.<\/p>\n\n\n\n

5. Uygulamalar<\/h3>\n\n\n\n

Makine \u00f6\u011frenimi e-posta gelen kutunuzda, bankan\u0131zda ve doktorunuzun muayenehanesinde zaten kullan\u0131l\u0131yor. Derin \u00f6\u011frenme teknolojisi ise, otomasyon ara\u00e7lar veya geli\u015fmi\u015f cerrahi uygulayan robotlar gibi daha karma\u015f\u0131k ve otonom programlar\u0131 m\u00fcmk\u00fcn k\u0131lar.<\/p>\n\n\n\n

Deep Learning (Derin \u00d6\u011frenme) \u00d6rnekleri<\/h2>\n\n\n\n
\"\"<\/figure><\/div>\n\n\n\n

Derin \u00f6\u011frenmenin g\u00fcnl\u00fck hayatta nas\u0131l kullan\u0131ld\u0131\u011f\u0131n\u0131 anlaman\u0131z i\u00e7in a\u015fa\u011f\u0131da sizin i\u00e7in baz\u0131 kullan\u0131m \u00f6rneklerini listeledik:<\/p>\n\n\n\n

1. Otomasyon ara\u00e7lar<\/h3>\n\n\n\n

Otomasyon ara\u00e7 teknolojisi, \u00f6n\u00fcm\u00fczdeki y\u0131llarda ger\u00e7ekle\u015fmesi beklenen en \u00f6nemli beklentilerden biridir. Bu beklentilerin kar\u015f\u0131lanmas\u0131 i\u00e7in otomotiv ara\u015ft\u0131rmac\u0131lar\u0131, ara\u00e7lar\u0131n dur i\u015faretleri ve trafik \u0131\u015f\u0131klar\u0131 gibi nesneleri otomatik olarak tespit etmesi i\u00e7in derin \u00f6\u011frenmeyi kullan\u0131r. Derin \u00f6\u011frenme kazalar\u0131 azaltmak i\u00e7in yoldaki yayalar\u0131 tespit etmeye de yard\u0131mc\u0131 olur.<\/p>\n\n\n\n

2. Havac\u0131l\u0131k ve savunma<\/h3>\n\n\n\n

Navigasyon sisteminin g\u00fcvenli ve emniyetsiz b\u00f6lgeleri uzun mesafeden tespit edebilmesi, savunma sanayi i\u00e7in \u00e7ok iyi bir uygulamad\u0131r. Bu y\u00fczden uydulara kurulan derin \u00f6\u011frenme, nesnelerin tan\u0131mlanmas\u0131na ve ilgi alanlar\u0131n\u0131n bulunmas\u0131na yard\u0131mc\u0131 olur.<\/p>\n\n\n\n

3. T\u0131bbi ara\u015ft\u0131rma<\/h3>\n\n\n\n

Derin \u00f6\u011frenme, kanser h\u00fccrelerini tespit etmede \u00f6nemli bir bile\u015fendir. Bu y\u00fczden, UCLA’daki kanser ara\u015ft\u0131rmac\u0131lar\u0131, kanser h\u00fccrelerini do\u011fru bir \u015fekilde tan\u0131mlamak i\u00e7in bir derin \u00f6\u011frenme uygulamas\u0131n\u0131 e\u011fiterek sonu\u00e7 veren geli\u015fmi\u015f bir mikroskop geli\u015ftirdi.<\/p>\n\n\n\n

4. End\u00fcstri otomasyonu<\/h3>\n\n\n\n

End\u00fcstrilerde derin \u00f6\u011frenme, g\u00fcvenli olmayan makineleri otomatik olarak bulmak i\u00e7in kullan\u0131l\u0131r ve insanlara konumdan uzakla\u015fmalar\u0131 i\u00e7in alarm verir. Bu, a\u011f\u0131r makine ortamlar\u0131nda \u00e7al\u0131\u015fanlar\u0131n g\u00fcvenli\u011fini sa\u011flar.<\/p>\n\n\n\n

5. Sosyal medya<\/h3>\n\n\n\n

Twitter, \u00fcr\u00fcnlerini geli\u015ftirmek i\u00e7in derin \u00f6\u011frenme algoritmalar\u0131 kullan\u0131r. Kullan\u0131c\u0131 tercihlerinin olas\u0131l\u0131klar\u0131 hakk\u0131nda zaman i\u00e7inde bilgi edinmek i\u00e7in derin sinir a\u011f\u0131yla bir\u00e7ok veriye eri\u015fir ve bunlar\u0131 analiz eder.<\/p>\n\n\n\n

Instagram, siber zorbal\u0131ktan ka\u00e7\u0131nmak i\u00e7in derin \u00f6\u011frenmeyi kullan\u0131r ve sinir bozucu yorumlar\u0131 siler.<\/p>\n\n\n\n

Facebook sayfalar\u0131, arkada\u015flar\u0131, \u00fcr\u00fcnleri vb. \u00f6nermek i\u00e7in derin \u00f6\u011frenmeyi kullan\u0131r. Dahas\u0131, Facebook m\u00fckemmel etiketlemeyi makul k\u0131lan y\u00fcz tan\u0131ma i\u00e7in YSA algoritmas\u0131n\u0131 kullan\u0131r.<\/p>\n\n\n\n

6. Sa\u011fl\u0131k hizmetleri<\/h3>\n\n\n\n

Derin \u00f6\u011frenme, sa\u011fl\u0131k hizmetlerinde h\u0131zla artan bir e\u011filimdir. \u00d6rne\u011fin, genel sa\u011fl\u0131k durumu, kan \u015fekeri seviyesi, kan bas\u0131nc\u0131, kalp at\u0131\u015f\u0131 say\u0131lar\u0131 ve di\u011fer \u00e7e\u015fitli \u00f6l\u00e7\u00fcmler gibi hasta durumlar\u0131 hakk\u0131nda ger\u00e7ek zamanl\u0131 bilgi sa\u011flamak i\u00e7in hasta verilerini kullanan giyilebilir sens\u00f6rler ve cihazlar derin \u00f6\u011frenmeyi kullan\u0131r.<\/p>\n\n\n\n

T\u0131bbi kurumlar, bireysel hastalar\u0131n sa\u011fl\u0131k durumunu incelemek i\u00e7in bu bilgilere eri\u015febilir. Bu teknoloji ayn\u0131 zamanda t\u0131bbi uzmanlar\u0131n verileri analiz etmesine ve geli\u015fmi\u015f t\u0131bbi te\u015fhisleri ve hasta bak\u0131m\u0131n\u0131 h\u0131zland\u0131ran e\u011filimleri tan\u0131mas\u0131na yard\u0131mc\u0131 olur.<\/p>\n\n\n\n

Ayr\u0131ca, derin \u00f6\u011frenme, uygun te\u015fhis, g\u00f6r\u00fcnt\u00fc b\u00f6l\u00fcmleme gibi \u00e7e\u015fitli ama\u00e7lar i\u00e7in ila\u00e7 ve t\u0131p \u015firketlerinde de yararl\u0131d\u0131r. \u00d6rne\u011fin, geleneksel sinir a\u011f\u0131 (CNN), MRI sonu\u00e7lar\u0131, X \u0131\u015f\u0131nlar\u0131, vb. g\u00f6r\u00fcnt\u00fcleri analiz etmek i\u00e7in kullan\u0131labilir.<\/p>\n","protected":false},"excerpt":{"rendered":"

Deep learning yani derin \u00f6\u011frenme, makine \u00f6\u011freniminin bir alt k\u00fcmesi olarak d\u00fc\u015f\u00fcn\u00fclebilir. Bilgisayar algoritmalar\u0131n\u0131 inceleyerek kendi kendine \u00f6\u011frenmeyi m\u00fcmk\u00fcn k\u0131lan ve geli\u015ftirmeye dayal\u0131 bir aland\u0131r….<\/p>\n","protected":false},"author":11,"featured_media":5712,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45],"tags":[],"class_list":["post-5703","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-internet"],"yoast_head":"\nDeep Learning (Derin \u00d6\u011frenme) Nedir? - Niobe Hosting<\/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:\/\/www.niobehosting.com\/blog\/deep-learning-derin-ogrenme\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning (Derin \u00d6\u011frenme) Nedir? - Niobe Hosting\" \/>\n<meta property=\"og:description\" content=\"Deep learning yani derin \u00f6\u011frenme, makine \u00f6\u011freniminin bir alt k\u00fcmesi olarak d\u00fc\u015f\u00fcn\u00fclebilir. 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