Cihazlar\u0131n ger\u00e7ek D\u00fcnya’daki sorunlar\u0131 \u00e7\u00f6zebilecek etkili tahminlerde bulunmas\u0131 sizi yeteri kadar heyecanland\u0131rm\u0131yorsa Deep Learning Nedir? sorusunu \u00e7\u0131lg\u0131n geli\u015fmelerle dolu zamanlarda sormak zorunda kalabilirsiniz:)<\/p>\n\n\n\n
S\u0131kl\u0131kla birbirinin yerine kullan\u0131lsa da deep learning (derin \u00f6\u011frenme) ve makine \u00f6\u011frenimi yapay zekan\u0131n iki \u00f6nemli par\u00e7as\u0131. Yapay zeka, insanlar gibi \u00e7al\u0131\u015fan ve tepki veren ak\u0131ll\u0131 makinelerin olu\u015fturulmas\u0131na katk\u0131da bulunan bir bilgisayar bilimi. <\/p>\n\n\n\n
Derin \u00f6\u011frenmenin, makine \u00f6\u011frenimine nazaran yapay zeka uygulamalar\u0131n\u0131n daha “insan benzeri” \u00f6zelliklerine g\u00fc\u00e7 veren bir yapay zeka bile\u015feni oldu\u011funu s\u00f6yleyebiliriz.<\/p>\n\n\n\n
Makine \u00f6\u011freniminin bir evrimi olarak kabul edilen derin \u00f6\u011frenme, asl\u0131nda makine \u00f6\u011freniminin bir alt alan\u0131, ancak ilgin\u00e7 olan makine \u00f6\u011freniminden daha geli\u015fmi\u015f olmas\u0131. <\/p>\n\n\n\n
Deep Learning Nedir?<\/h2>\n\n\n\n
Deep learning, herhangi bir insan m\u00fcdahalesi gerektirmeden; algoritmalar ve b\u00fcy\u00fck veri k\u00fcmeleri kullanarak kal\u0131plar bulan ve \u00e7\u0131kt\u0131lar olu\u015fturup yan\u0131tlar veren bir makine \u00f6\u011frenme tekni\u011fi. <\/p>\n\n\n\n
Cihazlara g\u00f6r\u00fcnt\u00fc, metin veya ses bi\u00e7imindeki girdileri filtrelemeyi, s\u0131n\u0131fland\u0131rmay\u0131 ve tahminlerde bulunmay\u0131 \u00f6\u011fretiyor. D\u00fcnyay\u0131 de\u011fi\u015ftiren bu teknoloji; bir bilgisayar\u0131n ald\u0131\u011f\u0131 bilgiler sayesinde \u00f6\u011frenip adapte olabilece\u011finin, bir makinenin kendi kendini d\u00fczeltebilece\u011finin kan\u0131t\u0131.<\/p>\n\n\n\n
Deep Learning, makinelerin; insanlar\u0131n yard\u0131m\u0131 olmadan do\u011fru kararlar vermesini sa\u011flayan “programlanabilir bir sinir a\u011f\u0131” kullan\u0131r ve ad\u0131n\u0131 yararland\u0131\u011f\u0131 bu derin<\/strong> sinir a\u011flar\u0131ndan al\u0131r.<\/p>\n\n\n\n
Bir yapay zeka algoritmas\u0131 yanl\u0131\u015f bir tahmin verdi\u011finde bir m\u00fchendis devreye girmeli ve ayarlamalar yapmal\u0131d\u0131r. Ancak derin \u00f6\u011frenme modeliyle, bir algoritma, bir tahminin do\u011fru olup olmad\u0131\u011f\u0131n\u0131 kendi sinir a\u011f\u0131 (neural network) arac\u0131l\u0131\u011f\u0131yla belirleyebilir.<\/p>\n\n\n\n
Derin \u00f6\u011frenme sistemleri, s\u00f6z konusu yapay sinir a\u011f\u0131 sayesinde, b\u00fcy\u00fck miktarda veriyle ba\u015fa \u00e7\u0131kabilir. Her g\u00fcn\u00fcn her saniyesinde inan\u0131lmaz miktarda veri toplayabilen geli\u015fmi\u015f ak\u0131ll\u0131 sens\u00f6rler \u00e7a\u011f\u0131nda ya\u015fad\u0131\u011f\u0131m\u0131z i\u00e7in her g\u00fcn 2,6 kentilyon bayt veri \u00fcretti\u011fimiz tahmin ediliyor. Bu muazzam miktardaki veriyle geleneksel bilgisayarlar\u0131n ba\u015fa \u00e7\u0131kmas\u0131 ve bu verilerden sonu\u00e7 \u00e7\u0131karmas\u0131 kolay olmad\u0131\u011f\u0131ndan; veri miktar\u0131 artt\u0131k\u00e7a daha verimli hale gelen derin \u00f6\u011frenme, y\u00fckselen trendler aras\u0131nda yerini sa\u011flamla\u015ft\u0131r\u0131yor. \u00c7\u00fcnk\u00fc ne kadar \u00e7ok veriyle \u00e7al\u0131\u015f\u0131rsa, sonu\u00e7lar o kadar kesin ve do\u011fru oluyor. <\/p>\n\n\n\n
Bu, derin \u00f6\u011frenmenin makine \u00f6\u011freniminden ayr\u0131ld\u0131\u011f\u0131 \u00f6nemli bir nokta. Derin \u00f6\u011frenme daha b\u00fcy\u00fck hacimli verilerle \u00f6l\u00e7eklenebilirken, makine \u00f6\u011frenimi;, belirli bir seviyeden sonra bir d\u00fczl\u00fc\u011fe ula\u015ft\u0131\u011f\u0131 s\u0131\u011f \u00f6\u011frenmeyle s\u0131n\u0131rl\u0131 kal\u0131yor ve daha fazla yeni veri eklenmesi hi\u00e7bir fark yaratm\u0131yor.<\/p>\n\n\n\n
Derin \u00f6\u011frenme uygulamalar\u0131, bir insan\u0131n eylemlerini g\u00f6zlemleyerek bir robotu e\u011fitebiliyor veya bir eylemi ger\u00e7ekle\u015ftirmek i\u00e7in di\u011fer bir\u00e7ok yapay zeka uygulamas\u0131ndan gelen ba\u011flant\u0131lar\u0131 kullanabiliyor. <\/p>\n\n\n\n
Derin \u00f6\u011frenmenin g\u00fcnl\u00fck hayat\u0131m\u0131z\u0131 nas\u0131l etkiledi\u011fini bilmek istiyorsan\u0131z; insan performans\u0131n\u0131 ba\u015far\u0131l\u0131 bir \u015fekilde taklit edebilen uygulamalar\u0131 d\u00fc\u015f\u00fcnebilirsiniz; otonom arabalar, sesli asistanlar ve dijital mecralardaki ki\u015fiselle\u015ftirilmi\u015f \u00f6neriler(Daha fazla \u00f6rne\u011fi yaz\u0131n\u0131n son b\u00f6l\u00fcm\u00fcnden okuyabilirsiniz).<\/p>\n\n\n\n
Derin \u00d6\u011frenmenin Tarihi<\/h2>\n\n\n\n
Derin \u00f6\u011frenme 1980’lerde Geoffrey Hinton taraf\u0131ndan kavramsalla\u015ft\u0131r\u0131ld\u0131. Hinton\u2019un derin \u00f6\u011frenmeye en b\u00fcy\u00fck katk\u0131s\u0131, makine \u00f6\u011frenimi tekniklerini insan beyniyle kar\u015f\u0131la\u015ft\u0131rmas\u0131 oldu. Hinton, beyindeki n\u00f6ronlar\u0131n yap\u0131s\u0131na benzer \u015fekilde yap\u0131land\u0131r\u0131lm\u0131\u015f bir derin \u00f6\u011frenme algoritmas\u0131 olan “sinir a\u011f\u0131” konseptini yaratt\u0131. <\/p>\n\n\n\n
Geoffrey Hinton’un sinir a\u011flar\u0131 konusundaki \u00e7\u0131\u011f\u0131r a\u00e7an ara\u015ft\u0131rmas\u0131 s\u0131ras\u0131nda sordu\u011fu soru, beyindeki n\u00f6ronlara benzer \u015fekilde davranan bilgisayar algoritmalar\u0131 olu\u015fturup olu\u015fturamayaca\u011f\u0131m\u0131zd\u0131.<\/p>\n\n\n\n
Derin \u00f6\u011frenmenin kavramsal do\u011fu\u015fu s\u0131ras\u0131nda, ara\u015ft\u0131rmac\u0131lar anlaml\u0131 derin \u00f6\u011frenme modelleri olu\u015fturmak ve e\u011fitmek i\u00e7in yeterli veriye veya hesaplama g\u00fcc\u00fcne eri\u015femiyorlard\u0131. Bu nedenle onlarca y\u0131l \u00f6nce bulunmas\u0131na ra\u011fmen, derin \u00f6\u011frenme konusundaki \u00e7al\u0131\u015fmalar\u0131n ivme kazanmas\u0131, i\u00e7inde bulundu\u011fumuz zamanlara kald\u0131.<\/p>\n\n\n\n
Derin \u00f6\u011frenmenin babas\u0131 kabul edilen Hinton’\u0131n \u015firketi DNNresearch, 2013 y\u0131l\u0131nda Google taraf\u0131ndan sat\u0131n al\u0131nd\u0131. Hinton da Google’da \u00e7al\u0131\u015fmaya devam etti.<\/p>\n\n\n\n
Derin \u00d6\u011frenme Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n\n\n\n<\/figure>\n\n\n\n
Bir derin \u00f6\u011frenme modeli, bir insan\u0131n nas\u0131l sonu\u00e7 \u00e7\u0131karaca\u011f\u0131na benzer bir mant\u0131k yap\u0131s\u0131yla verileri s\u00fcrekli olarak analiz etmek i\u00e7in tasarlanm\u0131\u015ft\u0131r. Bunu ba\u015farmak i\u00e7in, derin \u00f6\u011frenme uygulamalar\u0131 yapay sinir a\u011f\u0131 <\/strong>ad\u0131 verilen katmanl\u0131 bir algoritma yap\u0131s\u0131<\/strong> kullan\u0131r. Yapay sinir a\u011f\u0131n\u0131n tasar\u0131m\u0131, insan beyninin biyolojik sinir a\u011f\u0131ndan esinlenmi\u015ftir. Yapay sinir a\u011f\u0131; standart makine \u00f6\u011frenimi modellerinden \u00e7ok daha yetenekli bir \u00f6\u011frenme s\u00fcrecine yol a\u00e7ar.<\/p>\n\n\n\n
Makine \u00f6\u011freniminin birka\u00e7 katman sinir a\u011f\u0131 olabilirken derin \u00f6\u011frenmenin y\u00fczlerce veya binlerce katman\u0131 bulunur. Bu katmanlara, gelen b\u00fcy\u00fck miktarda veriyi i\u015flemek i\u00e7in ihtiyac\u0131 vard\u0131r. \u00c7\u00fcnk\u00fc yeterli katman olmadan, ortaya \u00e7\u0131kan sorunu \u00e7\u00f6zmeye yard\u0131mc\u0131 olacak yeterli n\u00f6ron olmaz. <\/p>\n\n\n\n
Derin \u00f6\u011frenme, \u00f6z\u00fcnde, makinelere insan zekas\u0131n\u0131 taklit etmeyi \u00f6\u011fretmek i\u00e7in yinelemeli y\u00f6ntemlere dayan\u0131r. Yapay bir sinir a\u011f\u0131, bu yinelemeli y\u00f6ntemi birka\u00e7 hiyerar\u015fik d\u00fczey arac\u0131l\u0131\u011f\u0131yla ger\u00e7ekle\u015ftirir. \u0130lk seviyeler, makinelerin basit bilgileri \u00f6\u011frenmesine yard\u0131mc\u0131 olur ve seviyeler artt\u0131k\u00e7a bilgi birikmeye devam eder. Her yeni seviye ile makineler daha fazla bilgi toplar ve bunlar\u0131 son seviyede \u00f6\u011frendikleriyle birle\u015ftirir. S\u00fcrecin sonunda, elde edilen bilgi bir nevi mant\u0131ksal d\u00fc\u015f\u00fcnce \u00f6rne\u011fi gibidir.<\/p>\n\n\n\n
Hiyerar\u015fik \u00f6\u011frenmede daha y\u00fcksek seviyeli, daha soyut veriler ve \u00f6zellikler; daha d\u00fc\u015f\u00fck seviyeli, daha az soyut \u00f6zellikler a\u00e7\u0131s\u0131ndan tan\u0131mlan\u0131r. Bu, algoritmalar\u0131n ve makinelerin ayr\u0131\u015ft\u0131r\u0131lmas\u0131 daha zor olan yeni verileri almas\u0131na ve bunlar\u0131 anla\u015f\u0131lmas\u0131 daha kolay olan ve halihaz\u0131rda kullan\u0131lm\u0131\u015f alanlara ay\u0131rmas\u0131na yard\u0131mc\u0131 olur.<\/p>\n\n\n\n
\u00d6rne\u011fin Siri gibi bir sesli asistan\u0131n konu\u015fmay\u0131 \u00f6\u011frenmesi i\u00e7in bu sesli asistan sinir a\u011f\u0131n\u0131n ilk seviyelerinde, ses yo\u011funlu\u011funu ve tonlamalar\u0131 belirlemeye \u00e7al\u0131\u015facakt\u0131r. Daha y\u00fcksek seviyeler i\u00e7in, kelime bilgisi toplayacak ve buna \u00f6nceki seviyelerin bulgular\u0131n\u0131 ekleyecektir. Sonraki seviyelerde, istemleri analiz edecek ve t\u00fcm sonu\u00e7lar\u0131n\u0131 birle\u015ftirecektir. Hiyerar\u015fik yap\u0131n\u0131n en \u00fcst seviyesi i\u00e7in, sesli asistan bir diyalo\u011fu analiz edebilecek ve bu girdiye dayanarak kar\u015f\u0131l\u0131k gelen bir eylem ger\u00e7ekle\u015ftirebilecek kadar konu\u015fmay\u0131 \u00f6\u011frenmi\u015f olacakt\u0131r.<\/p>\n\n\n\n
Derin \u00f6\u011frenme modelleri, i\u015fler yanl\u0131\u015f gitti\u011finde kendi kendine \u00f6\u011frenebilen ve m\u00fcdahale olmadan gerekli ayarlamalar\u0131 ve d\u00fczeltmeleri yapabilen denetimsiz \u00f6\u011frenme<\/strong> yetene\u011fine sahiptir.<\/p>\n\n\n\n
Verileri i\u015flerken, yapay sinir a\u011flar\u0131, son derece karma\u015f\u0131k matematiksel hesaplamalar\u0131 i\u00e7eren bir dizi ikili do\u011fru veya yanl\u0131\u015f sorudan al\u0131nan yan\u0131tlarla verileri s\u0131n\u0131fland\u0131rabilir. \u00d6rne\u011fin, bir y\u00fcz tan\u0131ma program\u0131, y\u00fczlerin kenarlar\u0131n\u0131 ve \u00e7izgilerini, ard\u0131ndan y\u00fczlerin daha \u00f6nemli k\u0131s\u0131mlar\u0131n\u0131 ve son olarak y\u00fczlerin genel temsillerini alg\u0131lamay\u0131 ve tan\u0131may\u0131 \u00f6\u011frenerek \u00e7al\u0131\u015f\u0131r. Zamanla program kendi kendini e\u011fitir ve do\u011fru yan\u0131t alma olas\u0131l\u0131\u011f\u0131 artar<\/span>, y\u00fczleri do\u011fru bir \u015fekilde tan\u0131mlayacak d\u00fczeye ula\u015f\u0131r.<\/p>\n\n\n\n
Derin \u00d6\u011frenme Neden \u00d6nemli?<\/h2>\n\n\n\n
Derin \u00f6\u011frenmenin, artan veri hacmini yinelemeli \u00f6\u011frenme modelleri arac\u0131l\u0131\u011f\u0131yla do\u011fru sonu\u00e7lar elde etmek i\u00e7in kullanmas\u0131; bulgulardaki hatalar\u0131 ve tutars\u0131zl\u0131klar\u0131 ortadan kald\u0131r\u0131p g\u00fcvenilir sonu\u00e7lara ula\u015fmam\u0131z\u0131 sa\u011fl\u0131yor.<\/p>\n\n\n\n
Derin \u00f6\u011frenme alan\u0131ndaki geli\u015fmeler, derin \u00f6\u011frenmenin bir\u00e7ok g\u00f6revi yerine getirmede insanlardan daha iyi noktaya ula\u015fabilmesini sa\u011flam\u0131\u015f durumda.<\/p>\n\n\n\n
Deep Learning; hisse senedi fiyatlar\u0131ndan hava durumuna, otomobiller i\u00e7in park yard\u0131m\u0131ndan y\u00fcz tan\u0131ma otomayonuna kadar kadar bir\u00e7ok alanda isabetli tahminler konusunda ustala\u015f\u0131yor. <\/p>\n\n\n\n
\u015eu ana kadar ba\u015fard\u0131klar\u0131na ra\u011fmen hen\u00fcz ba\u015flang\u0131\u00e7 a\u015famas\u0131nda diyebilece\u011fimiz deep learning, \u00f6n\u00fcm\u00fczdeki y\u0131llarda teknolojiyi k\u00f6kl\u00fc d\u00f6n\u00fc\u015f\u00fcmlere s\u00fcr\u00fckleyecek potansiyele sahip. <\/p>\n\n\n\n
Geleneksel y\u00f6ntemleri unutturacak geli\u015fmelerle ilerleyen derin \u00f6\u011frenme, makine \u00f6\u011frenimi algoritmas\u0131ndan; g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rmada % 41, y\u00fcz tan\u0131mada % 27 ve ses tan\u0131mada % 25 daha isabetli kararlara sahip.<\/p>\n\n\n\n
Makine \u00f6\u011freniminde en h\u0131zl\u0131 b\u00fcy\u00fcyen alan olarak kabul edilen derin \u00f6\u011frenme, \u00e7\u0131\u011f\u0131r a\u00e7an bir teknolojiyi temsil ediyor ve giderek daha fazla \u015firket taraf\u0131ndan yeni i\u015f modelleri olu\u015fturmak i\u00e7in kullan\u0131l\u0131yor.<\/p>\n\n\n\n
Derin \u00f6\u011frenme, yapay zekan\u0131n omurgas\u0131n\u0131 olu\u015fturan bilimsel bir mucize olarak kabul edilmektedir.<\/p>\n\n\n\n
G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda pek \u00e7ok otomasyonu besleyen derin \u00f6\u011frenmenin; kal\u0131p ke\u015ffinde (denetimsiz \u00f6\u011frenme) ve bilgiye dayal\u0131 tahminde yakalad\u0131\u011f\u0131 ba\u015far\u0131; kurulu\u015flar\u0131n \u00fcretkenlik, sat\u0131\u015f, y\u00f6netim ve yenilik a\u00e7\u0131s\u0131ndan benzeri g\u00f6r\u00fclmemi\u015f sonu\u00e7lar elde etmelerini sa\u011flayabiliyor.<\/p>\n\n\n\n
Gelecekte, derin \u00f6\u011frenmenin bilgisayar belle\u011finin \u00e7al\u0131\u015fma \u015feklini de\u011fi\u015ftirmesi, hayal etti\u011fimizden \u00e7ok daha geni\u015f depolama se\u00e7enekleri sunabilir. Derin \u00f6\u011frenme siber g\u00fcvenli\u011fi art\u0131rmaya da yard\u0131mc\u0131 olabilir.<\/p>\n\n\n\n
Derin \u00d6\u011frenme Uygulamalar\u0131 <\/h2>\n\n\n\n<\/figure>\n\n\n\n
Arka planda derin \u00f6\u011frenmenin rol ald\u0131\u011f\u0131 pek \u00e7ok uygulama aras\u0131nda Skype’\u0131n, s\u00f6zl\u00fc konu\u015fmalar\u0131 ger\u00e7ek zamanl\u0131 olarak \u00e7evirmesi, bir\u00e7ok e-posta platformunun spam iletileri gelen kutusuna ula\u015fmadan \u00f6nce tan\u0131mada ustala\u015fmas\u0131 ve PayPal’\u0131n, hileli \u00f6demeleri \u00f6nlemek i\u00e7in kulland\u0131\u011f\u0131 sistemi \u00f6rnek verebiliriz. Dahas\u0131 Google PlaNet’in, herhangi bir foto\u011fraf\u0131n nerede \u00e7ekildi\u011fini belirleyebilmesini, Netflix’in, ki\u015file\u015ftirilmi\u015f film se\u00e7enekleri sunabilmesini ekleyebiliriz.<\/p>\n\n\n\n
Saymakla bitiremesek de derin \u00f6\u011frenmenin son zamanlardaki en cazip uygulama alanlar\u0131n\u0131 s\u0131ralayal\u0131m:<\/p>\n\n\n\n
Derin \u00f6\u011frenme modelleri, otonom ara\u00e7lar\u0131n her g\u00fcn yolda kar\u015f\u0131la\u015fabilecekleri milyonlarca senaryoya haz\u0131rlanmas\u0131 i\u00e7in kullan\u0131l\u0131yor. Ara\u00e7lar\u0131n yoldayken nas\u0131l davranacaklar\u0131n\u0131 bilmelerine ve i\u00e7inde bulunduklar\u0131 durumlardan \u00f6\u011frenmelerine yard\u0131mc\u0131 oluyor. Test a\u015famas\u0131nda olan s\u00fcr\u00fcc\u00fcs\u00fcz otomobiller karma\u015f\u0131k sinir a\u011flar\u0131 katman\u0131 sayesinde ka\u00e7\u0131n\u0131lmas\u0131 gereken nesneleri belirleyebiliyor, trafik \u0131\u015f\u0131klar\u0131n\u0131 tan\u0131yor, h\u0131z\u0131 zamana g\u00f6re ayarlayacak \u015fekilde e\u011fitiliyor. \u0130leriye gitmenin veya hareketsiz kalman\u0131n ne zaman g\u00fcvenli oldu\u011funu belirleyebiliyor.<\/li><\/ul>\n\n\n\n
Derin \u00f6\u011frenme uygulamalar\u0131, hastalar i\u00e7in tedavi planlar\u0131 tasarlama ve kanser t\u00fcrlerinin erken tespiti i\u00e7in t\u0131bbi testlerde kullan\u0131l\u0131yor.<\/li><\/ul>\n\n\n\n
Konu\u015fma ve anlaman\u0131n bir par\u00e7as\u0131 olan t\u00fcm gramer ve s\u00f6z dizimi n\u00fcanslar\u0131n\u0131 anlamaya yard\u0131mc\u0131 olmak i\u00e7in derin \u00f6\u011frenme modelleri kullan\u0131l\u0131yor. Derin a\u011flar, nas\u0131l anlayacaklar\u0131n\u0131 ve uygun yan\u0131tlar\u0131 nas\u0131l bulacaklar\u0131n\u0131 \u00f6\u011frenmek i\u00e7in dili okuyor ve dinliyor. Bu yaz\u0131l\u0131mlar halihaz\u0131rda milyonlarca cihazda \u00e7eviri hizmetlerinde zaten kullan\u0131l\u0131yor. Siri, Cortana, Alexa ve Google Now gibi dijital asistanlar\u0131n da derin \u00f6\u011frenmeyi kulland\u0131\u011f\u0131n\u0131 tahmin edebiliyorsunuzdur. Google DeepMind\u2019\u0131n WaveNet’i ise do\u011fal insan sesini taklit ediyor ve \u015fu anda piyasada bulunan konu\u015fma sistemlerinden daha do\u011fal konu\u015fmalar \u00fcretebiliyor.<\/li><\/ul>\n\n\n\n
CamFind gibi uygulamalar, kullan\u0131c\u0131lar\u0131n herhangi bir nesnenin foto\u011fraf\u0131n\u0131 \u00e7ekmesine ve mobil g\u00f6rsel arama teknolojisini kullanarak nesnenin ne oldu\u011funu ke\u015ffetmesine olanak tan\u0131yor. Bu da elbette ki derin \u00f6\u011frenmeye dayan\u0131yor.<\/li><\/ul>\n\n\n\n
Google Deepmind\u2019\u0131n AlphaGo’su; keskin zeka ve sezgi gerektiren Go adl\u0131 oyunu oynamay\u0131 \u00f6\u011frenen kendi sinir a\u011f\u0131na sahip bir bilgisayar program\u0131. AlphaGo, oyunun d\u00fcnyaca \u00fcnl\u00fc bir\u00e7ok ustas\u0131n\u0131 yenerek bir cihaz\u0131n karma\u015f\u0131k teknikleri ve soyut y\u00f6nleri kavrayabilece\u011fini kan\u0131tlad\u0131.<\/li><\/ul>\n\n\n\n
Ge\u00e7mi\u015fte, siyah beyaz film g\u00f6r\u00fcnt\u00fclerinin elle renklendirilmesi gerekiyordu ve bu hem \u00e7ok zaman al\u0131c\u0131 hem de maliyetli bir i\u015fti. Art\u0131k bu i\u015flem, gri tonlamal\u0131 g\u00f6r\u00fcnt\u00fcleri otomatik olarak renklendirebilen derin \u00f6\u011frenme modelleriyle k\u0131sa s\u00fcrede yap\u0131labiliyor.<\/li><\/ul>\n\n\n\n
Derin \u00f6\u011frenmenin bir di\u011fer dikkat \u00e7ekici y\u00f6n\u00fc, bir g\u00f6r\u00fcnt\u00fcy\u00fc tan\u0131mlamak ve sanki bir insan altyaz\u0131y\u0131 yaz\u0131yormu\u015f gibi o g\u00f6r\u00fcnt\u00fc i\u00e7in uygun c\u00fcmle yap\u0131s\u0131yla anla\u015f\u0131l\u0131r bir ba\u015fl\u0131k olu\u015fturmak.<\/li><\/ul>\n\n\n\n
Derin \u00f6\u011frenme algoritmalar\u0131, bir metnin noktalama i\u015faretlerini, dilbilgisini ve stilini \u00f6\u011frenerek metin olu\u015fturabiliyor. Olu\u015fturdu\u011fu modeli, tamamen yeni bir metni olu\u015fturmak i\u00e7in kullanabiliyor.<\/li><\/ul>\n\n\n\n
\u00d6zet<\/h2>\n\n\n\n
Beyindeki n\u00f6ron a\u011flar\u0131n\u0131 taklit eden ve insan beyninin bilgiyi filtreleme \u015feklinden ilham alan derin \u00f6\u011frenme; finanstan pazarlamaya, t\u0131ptan e\u011flenceye say\u0131s\u0131z alanda ciddi d\u00f6n\u00fc\u015f\u00fcmlerin habercisi.<\/p>\n\n\n\n
Deep Learning Nedir? sorusuna; derin \u00f6\u011frenmenin heyecan verici gelece\u011fine dair g\u00f6stergeleri a\u00e7\u0131klayarak yan\u0131t vermeye \u00e7al\u0131\u015ft\u0131k. Bilgi teknolojileriyle ilgili alanlarda \u00e7al\u0131\u015f\u0131yorsan\u0131z, derin \u00f6\u011frenmenin bili\u015fimi nas\u0131l etkiledi\u011fini takipte kalman\u0131z\u0131 \u00f6neririz.<\/p>\n","protected":false},"excerpt":{"rendered":"D\u00fcnyay\u0131 de\u011fi\u015ftiren bu teknoloji; bir bilgisayar\u0131n ald\u0131\u011f\u0131 bilgiler sayesinde \u00f6\u011frenip adapte olabilece\u011finin, bir makinenin kendi kendini d\u00fczeltebilece\u011finin kan\u0131t\u0131.\n","protected":false},"author":1,"featured_media":26088,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"28132,45541,63991,30906,28980,29491","_relevanssi_noindex_reason":"","footnotes":""},"categories":[665,652,138,651],"tags":[],"class_list":{"0":"post-25765","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-bulut-sunucu","8":"category-dedicated-server","9":"category-ipuclari","10":"category-vds-server"},"yoast_head":"\n
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