{"id":34201,"date":"2025-11-12T21:31:25","date_gmt":"2025-11-12T18:31:25","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/"},"modified":"2025-11-12T21:31:25","modified_gmt":"2025-11-12T18:31:25","slug":"evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/","title":{"rendered":"Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) Nedir ve Neden \u00d6nemlidir?"},"content":{"rendered":"<h1>Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) Temelleri: Ba\u015flang\u0131\u00e7 Rehberi<\/h1>\n<p>G\u00fcn\u00fcm\u00fczde yapay zeka ve \u00f6zellikle derin \u00f6\u011frenme, hayat\u0131m\u0131z\u0131n bir\u00e7ok alan\u0131na dokunuyor. Peki, bilgisayarlar\u0131n g\u00f6r\u00fcnt\u00fcleri insan gibi anlamas\u0131n\u0131 sa\u011flayan Evri\u015fimsel Sinir A\u011flar\u0131 (CNN&#039;ler) nedir ve nas\u0131l \u00e7al\u0131\u015f\u0131r? Bu rehber, ba\u015flang\u0131\u00e7 seviyesinden karma\u015f\u0131k konulara do\u011fru ad\u0131m ad\u0131m ilerleyerek CNN&#039;lerin b\u00fcy\u00fcl\u00fc d\u00fcnyas\u0131n\u0131 size tan\u0131tacak.<\/p>\n<p>Hi\u00e7 d\u00fc\u015f\u00fcnd\u00fcn\u00fcz m\u00fc, bir bilgisayar ekrandaki bir kediyi nas\u0131l tan\u0131r ya da y\u00fcz\u00fcn\u00fcz\u00fc g\u00f6rerek telefonunuzun kilidini nas\u0131l a\u00e7ar? Bu t\u00fcr yeteneklerin arkas\u0131nda genellikle <a href=\"https:\/\/tr.wikipedia.org\/wiki\/Evri%C5%9Fimsel_sinir_a%C4%9F%C4%B1\" target=\"_blank\" rel=\"noopener\">Evri\u015fimsel Sinir A\u011flar\u0131<\/a> (Convolutional Neural Networks &#8211; CNN) ad\u0131 verilen \u00f6zel bir derin \u00f6\u011frenme modeli bulunur. Temel olarak, CNN&#039;ler \u00f6zellikle g\u00f6rsel verileri, yani resimleri ve videolar\u0131 i\u015flemek ve anlamland\u0131rmak i\u00e7in tasarlanm\u0131\u015f yapay sinir a\u011f\u0131 t\u00fcrleridir. Geleneksel yapay sinir a\u011flar\u0131n\u0131n aksine, CNN&#039;ler g\u00f6r\u00fcnt\u00fclerin iki boyutlu (veya daha y\u00fcksek boyutlu) yap\u0131s\u0131ndan faydalanarak \u00e7ok daha etkili \u00f6\u011frenme ve tan\u0131ma yetenekleri sunar.<\/p>\n<p>Peki, bu neden bu kadar \u00f6nemli? \u0130nsanl\u0131k olarak bilgiyi b\u00fcy\u00fck \u00f6l\u00e7\u00fcde g\u00f6rseller arac\u0131l\u0131\u011f\u0131yla alg\u0131lar\u0131z. Bilgisayarlar\u0131n da bu yetene\u011fe sahip olmas\u0131, bir\u00e7ok alanda devrim yaratm\u0131\u015ft\u0131r. \u00d6rne\u011fin, s\u00fcr\u00fcc\u00fcs\u00fcz arabalar \u00e7evreyi anlamak, t\u0131bbi g\u00f6r\u00fcnt\u00fcleme sistemleri hastal\u0131klar\u0131 te\u015fhis etmek, g\u00fcvenlik kameralar\u0131 \u015f\u00fcpheli durumlar\u0131 tespit etmek ve hatta sosyal medyada etiketleme \u00f6nerileri sunmak i\u00e7in CNN&#039;lerden faydalan\u0131r. Bu teknolojiler sayesinde, rutin g\u00f6revler otomatikle\u015ftirilir, insan hatas\u0131 minimize edilir ve daha \u00f6nce imkans\u0131z g\u00f6r\u00fcnen yenilikler m\u00fcmk\u00fcn hale gelir.<\/p>\n<p>Evri\u015fimsel Sinir A\u011flar\u0131, ilk olarak 1980&#039;lerde <a href=\"https:\/\/en.wikipedia.org\/wiki\/Yann_LeCun\" target=\"_blank\" rel=\"noopener\">Yann LeCun<\/a> taraf\u0131ndan geli\u015ftirilen &#8220;LeNet-5&#8221; gibi modellerle g\u00fcndeme gelmi\u015f olsa da, \u00f6zellikle 2012 y\u0131l\u0131ndaki <a href=\"https:\/\/en.wikipedia.org\/wiki\/ImageNet\" target=\"_blank\" rel=\"noopener\">ImageNet yar\u0131\u015fmas\u0131nda AlexNet<\/a> adl\u0131 bir CNN modelinin muazzam ba\u015far\u0131s\u0131yla derin \u00f6\u011frenme d\u00fcnyas\u0131nda bir d\u00f6n\u00fcm noktas\u0131 ya\u015fanm\u0131\u015ft\u0131r. O g\u00fcnden bu yana, donan\u0131m g\u00fcc\u00fcndeki art\u0131\u015f ve b\u00fcy\u00fck veri setlerinin eri\u015filebilirli\u011fi sayesinde CNN&#039;ler h\u0131zla geli\u015fti ve g\u00fcn\u00fcm\u00fczdeki yapay zeka uygulamalar\u0131n\u0131n temel ta\u015flar\u0131ndan biri haline geldi. Dolay\u0131s\u0131yla, bu alana ilgi duyan herkes i\u00e7in CNN&#039;leri anlamak, modern yapay zeka d\u00fcnyas\u0131n\u0131n kap\u0131lar\u0131n\u0131 aralamak anlam\u0131na gelmektedir.<\/p>\n<p>Bu makalede, bir ba\u015flang\u0131\u00e7 noktas\u0131 olarak, bir g\u00f6r\u00fcnt\u00fcy\u00fc anlamland\u0131rma s\u00fcrecinde Evri\u015fimsel Sinir A\u011flar\u0131n\u0131n temel bile\u015fenlerini ve \u00e7al\u0131\u015fma prensiplerini ad\u0131m ad\u0131m inceleyece\u011fiz. Geleneksel yapay zeka yakla\u015f\u0131mlar\u0131n\u0131n neden g\u00f6rsel verilerde yetersiz kald\u0131\u011f\u0131n\u0131 ve CNN&#039;lerin bu bo\u015flu\u011fu nas\u0131l doldurdu\u011funu daha iyi anlayacaks\u0131n\u0131z. Amac\u0131m\u0131z, karma\u015f\u0131k matematiksel detaylara bo\u011fulmadan, sezgisel bir yakla\u015f\u0131mla bu g\u00fc\u00e7l\u00fc teknolojinin nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131na dair sa\u011flam bir temel olu\u015fturmakt\u0131r.<\/p>\n<h2>CNN&#039;lerin Kalbindeki Temel Kavramlar: Katmanlar\u0131 Ke\u015ffedelim<\/h2>\n<p>Bir Evri\u015fimsel Sinir A\u011f\u0131, ad\u0131ndan da anla\u015f\u0131laca\u011f\u0131 gibi, \u00e7e\u015fitli katmanlardan olu\u015fur ve bu katmanlar ard\u0131\u015f\u0131k olarak \u00e7al\u0131\u015farak girdiyi i\u015fler, anlaml\u0131 \u00f6zellikler \u00e7\u0131kar\u0131r ve nihayetinde bir \u00e7\u0131kt\u0131 \u00fcretir. Bir CNN&#039;in temel yap\u0131 ta\u015flar\u0131 genellikle Evri\u015fim Katman\u0131, Aktivasyon Fonksiyonlar\u0131, Havuzlama Katman\u0131 ve Tam Ba\u011flant\u0131l\u0131 Katmanlard\u0131r. Bu katmanlar\u0131n her biri, g\u00f6r\u00fcnt\u00fcn\u00fcn farkl\u0131 bir y\u00f6n\u00fcn\u00fc analiz etmek ve bilgiyi bir sonraki a\u015famaya ta\u015f\u0131makla g\u00f6revlidir.<\/p>\n<p>\u0130lk olarak, <strong>Evri\u015fim Katman\u0131 (Convolutional Layer)<\/strong>, bir CNN&#039;in &#8220;beyni&#8221; olarak adland\u0131r\u0131labilir. Bu katman, girdi g\u00f6r\u00fcnt\u00fcs\u00fc \u00fczerinde k\u00fc\u00e7\u00fck &#8220;filtreler&#8221; veya &#8220;\u00e7ekirdekler&#8221; kullanarak, kenarlar, k\u00f6\u015feler, dokular gibi farkl\u0131 \u00f6zellik haritalar\u0131n\u0131 (feature maps) olu\u015fturur. Her bir filtre, g\u00f6r\u00fcnt\u00fcn\u00fcn belirli bir \u00f6zelli\u011fini tan\u0131mak \u00fczere \u00f6zelle\u015fmi\u015ftir. \u00d6rne\u011fin, bir filtre dikey kenarlar\u0131 alg\u0131larken, bir di\u011feri yatay kenarlar\u0131 veya belirli bir renk tonunu alg\u0131layabilir. Bu filtreler, g\u00f6r\u00fcnt\u00fcn\u00fcn her b\u00f6lgesini sistematik olarak tarayarak, o b\u00f6lgede belirledikleri \u00f6zellikleri bir sonraki katmana aktar\u0131r. Bu i\u015flem, bir ressam\u0131n tablonun farkl\u0131 k\u0131s\u0131mlar\u0131n\u0131 farkl\u0131 f\u0131r\u00e7alarla incelemesine benzer. Sonu\u00e7 olarak, ham piksel verisi yerine, g\u00f6r\u00fcnt\u00fcn\u00fcn daha soyut ve anlaml\u0131 bir temsili elde edilir.<\/p>\n<p>Evri\u015fim katman\u0131ndan hemen sonra genellikle bir <strong>Aktivasyon Fonksiyonu (Activation Function)<\/strong> gelir. En yayg\u0131n kullan\u0131lanlardan biri ReLU&#039;dur (Rectified Linear Unit). Aktivasyon fonksiyonlar\u0131n\u0131n temel amac\u0131, a\u011fa do\u011frusal olmayan \u00f6zellikler katmakt\u0131r. E\u011fer bu fonksiyonlar olmasayd\u0131, a\u011f ne kadar derin olursa olsun, sadece do\u011frusal ili\u015fkileri \u00f6\u011frenebilirdi ve bu da karma\u015f\u0131k g\u00f6r\u00fcnt\u00fclerdeki \u00f6r\u00fcnt\u00fcleri tan\u0131ma yetene\u011fini b\u00fcy\u00fck \u00f6l\u00e7\u00fcde k\u0131s\u0131tlard\u0131. ReLU, basit\u00e7e negatif de\u011ferleri s\u0131f\u0131ra indirirken, pozitif de\u011ferleri oldu\u011fu gibi b\u0131rak\u0131r. Bu, a\u011f\u0131n daha karma\u015f\u0131k ili\u015fkileri modellemesine yard\u0131mc\u0131 olur ve ayn\u0131 zamanda hesaplama y\u00fck\u00fcn\u00fc de azalt\u0131r.<\/p>\n<aside class=\"expert-tip\">\n  Uzman \u0130pucu: Aktivasyon fonksiyonlar\u0131, sinir a\u011flar\u0131n\u0131n &#8220;karar verme&#8221; s\u00fcre\u00e7lerine non-lineerlik ekleyerek, \u00e7ok daha karma\u015f\u0131k ve soyut \u00f6r\u00fcnt\u00fcleri \u00f6\u011frenmelerini sa\u011flar. ReLU, hem basitli\u011fi hem de hesaplama verimlili\u011fi nedeniyle \u00e7o\u011fu CNN mimarisinde varsay\u0131lan se\u00e7imdir.<br \/>\n<\/aside>\n<p>Bir sonraki \u00f6nemli katman, <strong>Havuzlama Katman\u0131 (Pooling Layer)<\/strong>&#039;d\u0131r. Evri\u015fim katman\u0131 sonras\u0131nda elde edilen \u00f6zellik haritalar\u0131 genellikle olduk\u00e7a b\u00fcy\u00fckt\u00fcr ve \u00e7ok fazla bilgi i\u00e7erir. Havuzlama katman\u0131, bu \u00f6zellik haritalar\u0131n\u0131n boyutunu k\u00fc\u00e7\u00fclterek hem hesaplama maliyetini d\u00fc\u015f\u00fcr\u00fcr hem de modelin a\u015f\u0131r\u0131 \u00f6\u011frenmesini (overfitting) engellemeye yard\u0131mc\u0131 olur. En yayg\u0131n havuzlama t\u00fcrleri Max Pooling ve Average Pooling&#039;dir. Max Pooling, belirlenen bir pencere i\u00e7erisindeki en y\u00fcksek de\u011feri al\u0131rken, Average Pooling ortalama de\u011feri al\u0131r. Bu i\u015flem sayesinde, g\u00f6r\u00fcnt\u00fcn\u00fcn \u00f6nemli \u00f6zelliklerini korurken, gereksiz detaylar at\u0131l\u0131r ve modelin konumdaki k\u00fc\u00e7\u00fck de\u011fi\u015fikliklere kar\u015f\u0131 daha diren\u00e7li olmas\u0131 sa\u011flan\u0131r. \u00d6rne\u011fin, bir kedinin resmi biraz sa\u011fa veya sola kaysa bile, havuzlama sayesinde model yine de kediyi tan\u0131yabilir.<\/p>\n<p>Son olarak, a\u011f\u0131n sonunda genellikle bir veya daha fazla <strong>Tam Ba\u011flant\u0131l\u0131 Katman (Fully Connected Layer)<\/strong> bulunur. Bu katmanlar, geleneksel yapay sinir a\u011flar\u0131ndaki katmanlara benzer \u015fekilde \u00e7al\u0131\u015f\u0131r. Havuzlama katmanlar\u0131ndan gelen boyutland\u0131r\u0131lm\u0131\u015f ve \u00f6zetlenmi\u015f \u00f6zellikler, bu katmanlara d\u00fczle\u015ftirilmi\u015f bir vekt\u00f6r olarak beslenir. Tam ba\u011flant\u0131l\u0131 katmanlar, \u00e7\u0131kar\u0131lan \u00f6zellikler aras\u0131ndaki karma\u015f\u0131k ili\u015fkileri \u00f6\u011frenir ve nihai s\u0131n\u0131fland\u0131rma veya regresyon g\u00f6revini ger\u00e7ekle\u015ftirir. \u00d6rne\u011fin, bir kediyi mi yoksa k\u00f6pe\u011fi mi g\u00f6sterdi\u011fini belirlemek i\u00e7in bu katmanlar, \u00f6nceki katmanlardan gelen &#8220;b\u0131y\u0131k&#8221; veya &#8220;burun \u015fekli&#8221; gibi \u00f6zelliklere dayanarak bir karar verir. Sonu\u00e7 olarak, a\u011f\u0131n \u00e7\u0131kt\u0131s\u0131 genellikle farkl\u0131 s\u0131n\u0131flara ait olma olas\u0131l\u0131klar\u0131n\u0131 g\u00f6steren bir olas\u0131l\u0131k da\u011f\u0131l\u0131m\u0131d\u0131r.<\/p>\n<p>Bu temel katmanlar, bir Evri\u015fimsel Sinir A\u011f\u0131n\u0131n kalbini olu\u015fturur ve g\u00f6rsel verileri anlaml\u0131 bilgilere d\u00f6n\u00fc\u015ft\u00fcrme s\u00fcrecinde kritik rol oynar. Her katman, bir \u00f6nceki katman\u0131n \u00e7\u0131kt\u0131s\u0131n\u0131 al\u0131r, \u00fczerinde belirli bir d\u00f6n\u00fc\u015f\u00fcm yapar ve bir sonraki katmana daha soyut ve i\u015flenmi\u015f bir bilgi aktar\u0131r. Bu katmanlar aras\u0131ndaki sinerji sayesinde, CNN&#039;ler inan\u0131lmaz derecede karma\u015f\u0131k g\u00f6rsel tan\u0131ma g\u00f6revlerini ba\u015far\u0131yla yerine getirebilir.<\/p>\n<h3>Peki, Evri\u015fim Katman\u0131 G\u00f6r\u00fcnt\u00fcleri Nas\u0131l Anlar?<\/h3>\n<p>Evri\u015fim katman\u0131, CNN&#039;lerin en benzersiz ve kritik bile\u015fenidir. G\u00f6r\u00fcnt\u00fcleri anlaman\u0131n anahtar\u0131, bu katman\u0131n &#8220;filtreler&#8221; veya &#8220;\u00e7ekirdekler&#8221; ad\u0131 verilen k\u00fc\u00e7\u00fck matrisleri nas\u0131l kulland\u0131\u011f\u0131nda yatar. Bir filtre, asl\u0131nda bir t\u00fcr detekt\u00f6rd\u00fcr ve belirli bir \u00f6zelli\u011fi, \u00f6rne\u011fin bir kenar\u0131, bir k\u00f6\u015feyi veya bir deseni aramaya programlanm\u0131\u015ft\u0131r. Bu filtreler, t\u00fcm g\u00f6r\u00fcnt\u00fc \u00fczerinde k\u00fc\u00e7\u00fck ad\u0131mlarla kayd\u0131r\u0131larak (evri\u015fim i\u015flemi), g\u00f6r\u00fcnt\u00fcn\u00fcn her b\u00f6lgesindeki bu \u00f6zellikleri tespit eder.<\/p>\n<p>Bu s\u00fcreci daha iyi anlamak i\u00e7in bir \u00f6rnek d\u00fc\u015f\u00fcnelim: Elimizde 5&#215;5 piksellik basit bir gri tonlamal\u0131 g\u00f6r\u00fcnt\u00fc ve 3&#215;3 piksellik bir filtre olsun. G\u00f6r\u00fcnt\u00fcdeki her pikselin bir yo\u011funluk de\u011feri (0-255 aras\u0131) varken, filtredeki her de\u011fer de bir a\u011f\u0131rl\u0131\u011f\u0131 temsil eder. Evri\u015fim i\u015flemi s\u0131ras\u0131nda, filtre g\u00f6r\u00fcnt\u00fcn\u00fcn sol \u00fcst k\u00f6\u015fesinden ba\u015flar. Filtrenin pikselleri ile g\u00f6r\u00fcnt\u00fcn\u00fcn o an kapsad\u0131\u011f\u0131 3&#215;3 piksellik alan\u0131n pikselleri eleman eleman \u00e7arp\u0131l\u0131r ve t\u00fcm \u00e7arp\u0131m sonu\u00e7lar\u0131 toplan\u0131r. Bu toplam, \u00e7\u0131kt\u0131 \u00f6zelli\u011finin (feature map) o b\u00f6lgesindeki bir pikselin de\u011feri olur. Filtre daha sonra sa\u011fa do\u011fru belirli bir ad\u0131m (stride) say\u0131s\u0131 kadar kayar ve ayn\u0131 i\u015flem tekrarlan\u0131r. Sat\u0131r\u0131n sonuna gelindi\u011finde, filtre bir sonraki sat\u0131ra iner ve bu s\u00fcre\u00e7 t\u00fcm g\u00f6r\u00fcnt\u00fc taranana kadar devam eder.<\/p>\n<p>Bu kayd\u0131rma i\u015flemi s\u0131ras\u0131nda iki \u00f6nemli parametre vard\u0131r: <strong>Ad\u0131m (Stride)<\/strong> ve <strong>Doldurma (Padding)<\/strong>. Stride, filtrenin g\u00f6r\u00fcnt\u00fc \u00fczerinde ne kadar ad\u0131m ataca\u011f\u0131n\u0131 belirler. B\u00fcy\u00fck bir stride de\u011feri, \u00f6zellik haritas\u0131n\u0131n boyutunu k\u00fc\u00e7\u00fclt\u00fcrken, k\u00fc\u00e7\u00fck bir stride daha detayl\u0131 bir \u00e7\u0131kt\u0131 verir. Padding ise, g\u00f6r\u00fcnt\u00fcn\u00fcn kenarlar\u0131na s\u0131f\u0131r pikseller ekleme i\u015flemidir. Bunun iki ana nedeni vard\u0131r: birincisi, evri\u015fim i\u015flemi s\u0131ras\u0131nda g\u00f6r\u00fcnt\u00fcn\u00fcn kenarlar\u0131ndaki bilgi kayb\u0131n\u0131 \u00f6nlemek; ikincisi ise, \u00e7\u0131kt\u0131 \u00f6zellik haritas\u0131n\u0131n boyutunu kontrol alt\u0131nda tutmak. Genellikle &#8220;same&#8221; padding kullan\u0131larak, \u00e7\u0131kt\u0131 \u00f6zellik haritas\u0131n\u0131n boyutu girdi g\u00f6r\u00fcnt\u00fcs\u00fc ile ayn\u0131 tutulmaya \u00e7al\u0131\u015f\u0131l\u0131r.<\/p>\n<p>Birden fazla filtre kullan\u0131ld\u0131\u011f\u0131nda, her bir filtre farkl\u0131 bir \u00f6zelli\u011fi tespit eder ve bu da bize farkl\u0131 \u00f6zellik haritalar\u0131 verir. \u00d6rne\u011fin, bir filtre dikey kenarlar\u0131 vurgularken, ba\u015fka bir filtre yatay kenarlar\u0131 veya \u00e7apraz \u00e7izgileri vurgulayabilir. Bu \u00f6zellik haritalar\u0131 bir araya getirildi\u011finde, a\u011f, g\u00f6r\u00fcnt\u00fcn\u00fcn g\u00f6rsel i\u00e7eri\u011fine dair zengin ve \u00e7e\u015fitli bir anlay\u0131\u015f geli\u015ftirir. Her bir sonraki evri\u015fim katman\u0131, bir \u00f6nceki katmandan elde edilen daha basit \u00f6zellik haritalar\u0131n\u0131 girdi olarak al\u0131r ve bunlardan daha karma\u015f\u0131k, soyut \u00f6zellikler (\u00f6rne\u011fin, g\u00f6z, burun, tekerlek gibi par\u00e7alar) \u00e7\u0131karmay\u0131 \u00f6\u011frenir. Bu hiyerar\u015fik \u00f6zellik \u00f6\u011frenme yap\u0131s\u0131, CNN&#039;lerin inan\u0131lmaz ba\u015far\u0131s\u0131n\u0131n temelinde yatan prensiptir.<\/p>\n<pre><code>\n# Basit bir 2D evri\u015fim i\u015flemi nas\u0131l g\u00f6rselle\u015ftirilebilir (pseudocode)\n\ngirdi_g\u00f6r\u00fcnt\u00fcs\u00fc = [\n    [1, 1, 1, 0, 0],\n    [0, 1, 1, 1, 0],\n    [0, 0, 1, 1, 1],\n    [0, 0, 1, 1, 0],\n    [0, 1, 1, 0, 0]\n]\n\nfiltre = [\n    [1, 0, 1],\n    [0, 1, 0],\n    [1, 0, 1]\n]\n\n\u00e7\u0131kt\u0131_\u00f6zellik_haritas\u0131 = []\n\n# Evri\u015fim i\u015flemi i\u00e7in basit bir d\u00f6ng\u00fc\n# Stride = 1, Padding = 0 (\u00f6rnek basitle\u015ftirmesi)\n\nfor i in range(len(girdi_g\u00f6r\u00fcnt\u00fcs\u00fc) - len(filtre) + 1):\n    sat\u0131r_\u00e7\u0131kt\u0131s\u0131 = []\n    for j in range(len(girdi_g\u00f6r\u00fcnt\u00fcs\u00fc[0]) - len(filtre[0]) + 1):\n        toplam = 0\n        for fi in range(len(filtre)):\n            for fj in range(len(filtre[0])):\n                toplam += girdi_g\u00f6r\u00fcnt\u00fcs\u00fc[i + fi][j + fj] * filtre[fi][fj]\n        sat\u0131r_\u00e7\u0131kt\u0131s\u0131.append(toplam)\n    \u00e7\u0131kt\u0131_\u00f6zellik_haritas\u0131.append(sat\u0131r_\u00e7\u0131kt\u0131s\u0131)\n\n# print(\"\u00c7\u0131kt\u0131 \u00d6zellik Haritas\u0131:\", \u00e7\u0131kt\u0131_\u00f6zellik_haritas\u0131)\n# Bu \u00f6rnek, bir 5x5 girdi g\u00f6r\u00fcnt\u00fcs\u00fc ve 3x3 filtreden 3x3 \u00e7\u0131kt\u0131 haritas\u0131 \u00fcretir.\n# \u00c7\u0131kt\u0131 \u00d6zellik Haritas\u0131: [[4, 3, 4], [2, 4, 3], [2, 3, 4]]\n<\/code><\/pre>\n<h3>CNN Nas\u0131l \u00c7al\u0131\u015f\u0131r? Ad\u0131m Ad\u0131m Bir Modelin E\u011fitimi<\/h3>\n<p>Bir Evri\u015fimsel Sinir A\u011f\u0131&#039;n\u0131n (CNN) ger\u00e7ek d\u00fcnyada \u00e7al\u0131\u015f\u0131r hale gelmesi i\u00e7in kapsaml\u0131 bir e\u011fitim s\u00fcrecinden ge\u00e7mesi gerekir. Bu s\u00fcre\u00e7, temelde modelin veriden \u00f6\u011frenmesini ve belirli bir g\u00f6revi (\u00f6rne\u011fin, bir g\u00f6r\u00fcnt\u00fcy\u00fc s\u0131n\u0131fland\u0131rmak) yerine getirme yetene\u011fini geli\u015ftirmesini i\u00e7erir. Ad\u0131m ad\u0131m bu s\u00fcreci inceleyelim:<\/p>\n<ol>\n<li><strong>Veri Seti Haz\u0131rl\u0131\u011f\u0131:<\/strong> Herhangi bir makine \u00f6\u011frenimi modelinde oldu\u011fu gibi, CNN&#039;lerin de \u00f6\u011frenmek i\u00e7in kaliteli ve yeterli veriye ihtiyac\u0131 vard\u0131r. G\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma \u00f6rne\u011fi \u00fczerinden gidecek olursak, modelin kedileri ve k\u00f6pekleri ay\u0131rt etmesini istiyorsak, ona binlerce etiketli kedi ve k\u00f6pek resmi g\u00f6stermemiz gerekir. Bu veri setleri genellikle \u00fc\u00e7 ana par\u00e7aya ayr\u0131l\u0131r:\n<ul>\n<li><strong>E\u011fitim Seti:<\/strong> Modelin \u00f6\u011frenme yapaca\u011f\u0131 ana veri k\u00fcmesi.<\/li>\n<li><strong>Do\u011frulama (Validasyon) Seti:<\/strong> E\u011fitimin her a\u015famas\u0131nda modelin performans\u0131n\u0131 de\u011ferlendirmek ve hiperparametreleri ayarlamak i\u00e7in kullan\u0131l\u0131r. Modelin hi\u00e7 g\u00f6rmedi\u011fi verilere nas\u0131l genelledi\u011fini g\u00f6sterir.<\/li>\n<li><strong>Test Seti:<\/strong> Modelin nihai performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in kullan\u0131lan, e\u011fitim ve do\u011frulama s\u0131ras\u0131nda hi\u00e7 kullan\u0131lmam\u0131\u015f verilerdir. Bu, modelin ger\u00e7ek d\u00fcnyada ne kadar iyi \u00e7al\u0131\u015faca\u011f\u0131n\u0131 g\u00f6steren ba\u011f\u0131ms\u0131z bir \u00f6l\u00e7\u00fctt\u00fcr.<\/li>\n<\/ul>\n<p>        G\u00f6r\u00fcnt\u00fcler genellikle piksellerin say\u0131sal de\u011ferleri olarak temsil edilir ve a\u011fa beslenmeden \u00f6nce normalle\u015ftirme gibi \u00f6n i\u015fleme ad\u0131mlar\u0131ndan ge\u00e7er.<\/li>\n<li><strong>Model Mimarisi Olu\u015fturma:<\/strong> Bu a\u015famada, kullanaca\u011f\u0131m\u0131z evri\u015fim katmanlar\u0131n\u0131n, havuzlama katmanlar\u0131n\u0131n ve tam ba\u011flant\u0131l\u0131 katmanlar\u0131n say\u0131s\u0131, s\u0131ralamas\u0131 ve her bir katman\u0131n parametreleri (filtre say\u0131s\u0131, boyutu, stride, padding vb.) belirlenir. Bu mimari, genellikle deneyim ve mevcut literat\u00fcrdeki ba\u015far\u0131l\u0131 modellerden ilham al\u0131narak tasarlan\u0131r. Keras veya TensorFlow gibi derin \u00f6\u011frenme k\u00fct\u00fcphaneleri, bu mimariyi kodla tan\u0131mlamay\u0131 olduk\u00e7a kolayla\u015ft\u0131r\u0131r.<\/li>\n<li><strong>E\u011fitim S\u00fcreci (\u0130leri ve Geri Yay\u0131l\u0131m):<\/strong>\n<ul>\n<li><strong>\u0130leri Yay\u0131l\u0131m (Forward Propagation):<\/strong> E\u011fitim setinden bir grup (batch) g\u00f6r\u00fcnt\u00fc al\u0131n\u0131r ve modelin katmanlar\u0131ndan s\u0131rayla ge\u00e7irilir. Her katman, bir \u00f6nceki katman\u0131n \u00e7\u0131kt\u0131s\u0131n\u0131 i\u015fler ve bir sonraki katmana aktar\u0131r. En son katmana ula\u015f\u0131ld\u0131\u011f\u0131nda, model bir tahminde bulunur (\u00f6rne\u011fin, &#8220;bu %90 kedi, %10 k\u00f6pek&#8221;).<\/li>\n<li><strong>Kay\u0131p Fonksiyonu (Loss Function):<\/strong> Modelin tahmini ile ger\u00e7ek etiket (ground truth) aras\u0131ndaki fark\u0131 \u00f6l\u00e7en bir kay\u0131p fonksiyonu kullan\u0131l\u0131r. \u00d6rne\u011fin, tahmin edilen &#8220;kedi&#8221; olas\u0131l\u0131\u011f\u0131 0.9 iken, ger\u00e7ekte bu bir &#8220;k\u00f6pek&#8221; ise, kay\u0131p fonksiyonu y\u00fcksek bir hata de\u011feri d\u00f6nd\u00fcr\u00fcr. Bu de\u011fer, modelin ne kadar k\u00f6t\u00fc tahmin yapt\u0131\u011f\u0131n\u0131 g\u00f6sterir.<\/li>\n<li><strong>Geri Yay\u0131l\u0131m (Backpropagation):<\/strong> Kay\u0131p fonksiyonundan gelen hata de\u011feri, a\u011f\u0131n gerisine do\u011fru, katman katman yay\u0131l\u0131r. Bu i\u015flem s\u0131ras\u0131nda, her bir katmandaki filtre a\u011f\u0131rl\u0131klar\u0131n\u0131n ve di\u011fer parametrelerin hataya ne kadar katk\u0131da bulundu\u011fu hesaplan\u0131r.<\/li>\n<li><strong>Optimizasyon (Optimization):<\/strong> Geri yay\u0131l\u0131m s\u0131ras\u0131nda hesaplanan gradyanlar (hataya g\u00f6re parametrelerin de\u011fi\u015fim oran\u0131) kullan\u0131larak, modelin a\u011f\u0131rl\u0131klar\u0131 ve \u00f6nyarg\u0131lar\u0131 (bias) g\u00fcncellenir. Bu g\u00fcncelleme i\u015flemi, modelin bir sonraki iterasyonda daha do\u011fru tahminler yapmas\u0131n\u0131 sa\u011flamak amac\u0131yla yap\u0131l\u0131r. <a href=\"https:\/\/tr.wikipedia.org\/wiki\/Adam_(yaz%C4%B1l%C4%B1m_mimarisi)\" target=\"_blank\" rel=\"noopener\">Adam<\/a> veya SGD gibi optimizasyon algoritmalar\u0131, bu g\u00fcncelleme s\u00fcrecini y\u00f6netir. Bu d\u00f6ng\u00fc (ileri yay\u0131l\u0131m, kay\u0131p hesaplama, geri yay\u0131l\u0131m, optimizasyon) binlerce veya milyonlarca kez tekrarlan\u0131r. Her tekrara bir &#8220;epoch&#8221; denir ve her epoch&#039;ta model t\u00fcm e\u011fitim verisini en az bir kez g\u00f6rm\u00fc\u015f olur.<\/li>\n<\/ul>\n<\/li>\n<li><strong>De\u011ferlendirme:<\/strong> E\u011fitim s\u00fcreci tamamland\u0131\u011f\u0131nda veya belirli aral\u0131klarla, modelin performans\u0131 do\u011frulama seti \u00fczerinde de\u011ferlendirilir. Bu, modelin a\u015f\u0131r\u0131 \u00f6\u011frenip \u00f6\u011frenmedi\u011fini (yani sadece e\u011fitim verisini ezberleyip genelleme yapamamas\u0131n\u0131) kontrol etmemizi sa\u011flar. Son olarak, modelin ger\u00e7ek d\u00fcnya performans\u0131n\u0131 \u00f6l\u00e7mek i\u00e7in test seti kullan\u0131l\u0131r.<\/li>\n<\/ol>\n<p>Bu s\u00fcre\u00e7, bir bebe\u011fin d\u00fcnyay\u0131 deneyimleyerek \u00f6\u011frenmesine benzer. \u00d6nce nesneleri g\u00f6r\u00fcr (ileri yay\u0131l\u0131m), hata yapar (kay\u0131p fonksiyonu), hatalar\u0131ndan ders \u00e7\u0131kar\u0131r (geri yay\u0131l\u0131m) ve bilgiyi kullanarak gelecekte daha iyi kararlar verir (optimizasyon). Bu d\u00f6ng\u00fcsel \u00f6\u011frenme sayesinde, Evri\u015fimsel Sinir A\u011flar\u0131, karma\u015f\u0131k g\u00f6rsel g\u00f6revlerde inan\u0131lmaz yetenekler geli\u015ftirebilir.<\/p>\n<pre><code class=\"language-python\">\n# Basit bir CNN model mimarisi \u00f6rne\u011fi (Keras ile)\nfrom tensorflow.keras import layers, models\n\ndef create_simple_cnn(input_shape=(32, 32, 3), num_classes=10):\n    model = models.Sequential()\n    \n    # \u0130lk Evri\u015fim Katman\u0131\n    model.add(layers.Conv2D(32, (3, 3), activation=&#039;relu&#039;, input_shape=input_shape))\n    model.add(layers.MaxPooling2D((2, 2)))\n    \n    # \u0130kinci Evri\u015fim Katman\u0131\n    model.add(layers.Conv2D(64, (3, 3), activation=&#039;relu&#039;))\n    model.add(layers.MaxPooling2D((2, 2)))\n    \n    # \u00dc\u00e7\u00fcnc\u00fc Evri\u015fim Katman\u0131 (iste\u011fe ba\u011fl\u0131)\n    model.add(layers.Conv2D(64, (3, 3), activation=&#039;relu&#039;))\n    \n    # Modelin &#039;derin&#039; k\u0131sm\u0131na ge\u00e7meden \u00f6nce veriyi d\u00fczle\u015ftirme\n    model.add(layers.Flatten())\n    \n    # Tam Ba\u011flant\u0131l\u0131 Katmanlar\n    model.add(layers.Dense(64, activation=&#039;relu&#039;))\n    model.add(layers.Dense(num_classes, activation=&#039;softmax&#039;)) # \u00c7\u0131k\u0131\u015f katman\u0131 (s\u0131n\u0131fland\u0131rma i\u00e7in)\n    \n    return model\n\n# Modeli olu\u015ftur ve \u00f6zetini yazd\u0131r\ncnn_model = create_simple_cnn()\n# cnn_model.summary()\n\n# Modeli derleme (compile)\n# cnn_model.compile(optimizer=&#039;adam&#039;,\n#                   loss=&#039;sparse_categorical_crossentropy&#039;,\n#                   metrics=[&#039;accuracy&#039;])\n\n# E\u011fitim i\u00e7in veri y\u00fckleme ve model.fit() kullanma ad\u0131mlar\u0131 takip eder.\n<\/code><\/pre>\n<h2>CNN&#039;lerin Ger\u00e7ek D\u00fcnya Uygulamalar\u0131: Nerede Kar\u015f\u0131m\u0131za \u00c7\u0131k\u0131yorlar?<\/h2>\n<p>Evri\u015fimsel Sinir A\u011flar\u0131 (CNN), sadece akademik bir merak olman\u0131n \u00f6tesinde, g\u00fcnl\u00fck hayat\u0131m\u0131zda kar\u015f\u0131la\u015ft\u0131\u011f\u0131m\u0131z bir\u00e7ok teknolojik uygulaman\u0131n bel kemi\u011fini olu\u015fturur. G\u00f6rsel verileri anlama ve i\u015fleme yetenekleri sayesinde, sekt\u00f6rden sekt\u00f6re yay\u0131lan geni\u015f bir kullan\u0131m alan\u0131na sahiptirler. \u0130\u015fte CNN&#039;lerin en \u00e7arp\u0131c\u0131 ger\u00e7ek d\u00fcnya uygulamalar\u0131ndan baz\u0131lar\u0131:<\/p>\n<ul>\n<li>\n        <strong>G\u00f6r\u00fcnt\u00fc S\u0131n\u0131fland\u0131rma ve Etiketleme:<\/strong> Belki de CNN&#039;lerin en bilinen uygulamas\u0131d\u0131r. Bir g\u00f6r\u00fcnt\u00fcdeki ana objeyi veya sahneyi tan\u0131mlamak i\u00e7in kullan\u0131l\u0131rlar.<\/p>\n<ul>\n<li><strong>Sosyal Medya:<\/strong> Facebook, Instagram gibi platformlar, y\u00fckledi\u011finiz foto\u011fraflardaki arkada\u015flar\u0131n\u0131z\u0131 veya nesneleri otomatik olarak etiketlemek i\u00e7in CNN&#039;leri kullan\u0131r.<\/li>\n<li><strong>T\u0131bbi Te\u015fhis:<\/strong> Radyoloji alan\u0131nda, X-ray, MR veya CT taramalar\u0131ndaki anormallikleri (\u00f6rne\u011fin, t\u00fcm\u00f6rleri veya hastal\u0131k belirtilerini) s\u0131n\u0131fland\u0131rmak ve doktorlara yard\u0131mc\u0131 olmak i\u00e7in kullan\u0131l\u0131r. Bu, erken te\u015fhisin anahtar\u0131n\u0131 olu\u015fturabilir.<\/li>\n<li><strong>Kalite Kontrol:<\/strong> \u00dcretim hatlar\u0131nda, \u00fcr\u00fcnlerdeki kusurlar\u0131 otomatik olarak tespit etmek i\u00e7in kullan\u0131l\u0131r.<\/li>\n<\/ul>\n<\/li>\n<li>\n        <strong>Nesne Tespiti (Object Detection):<\/strong> G\u00f6r\u00fcnt\u00fcdeki birden fazla nesneyi hem tan\u0131mlar hem de konumlar\u0131n\u0131 belirler (s\u0131n\u0131r kutular\u0131 ile). Bu, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rmadan daha karma\u015f\u0131k bir g\u00f6revdir.<\/p>\n<ul>\n<li><strong>Otonom Ara\u00e7lar:<\/strong> S\u00fcr\u00fcc\u00fcs\u00fcz arabalar, yoldaki di\u011fer ara\u00e7lar\u0131, yayalar\u0131, trafik i\u015faretlerini ve \u015feritleri ger\u00e7ek zamanl\u0131 olarak alg\u0131lamak i\u00e7in CNN tabanl\u0131 nesne tespit sistemleri kullan\u0131r. Bu, g\u00fcvenli s\u00fcr\u00fc\u015f i\u00e7in hayati \u00f6neme sahiptir.<\/li>\n<li><strong>G\u00fcvenlik ve G\u00f6zetim:<\/strong> Kalabal\u0131k ortamlarda \u015f\u00fcpheli nesneleri veya olaylar\u0131 (\u00f6rne\u011fin, unutulmu\u015f paketler, d\u00fc\u015fen insanlar) tespit etmek i\u00e7in kullan\u0131l\u0131r.<\/li>\n<li><strong>Perakende:<\/strong> Ma\u011fazalarda raf stoklar\u0131n\u0131 izlemek, m\u00fc\u015fterilerin al\u0131\u015fveri\u015f davran\u0131\u015flar\u0131n\u0131 analiz etmek veya h\u0131rs\u0131zl\u0131\u011f\u0131 \u00f6nlemek i\u00e7in kullan\u0131labilir.<\/li>\n<\/ul>\n<\/li>\n<li>\n        <strong>Y\u00fcz Tan\u0131ma ve Duygu Analizi:<\/strong> Bireylerin kimli\u011fini belirlemek veya y\u00fcz ifadelerinden duygular\u0131 \u00e7\u0131karmak i\u00e7in kullan\u0131l\u0131r.<\/p>\n<ul>\n<li><strong>Ak\u0131ll\u0131 Telefonlar ve Kilit Sistemleri:<\/strong> Telefonunuzun kilidini y\u00fcz\u00fcn\u00fczle a\u00e7man\u0131z veya bir binaya y\u00fcz tan\u0131ma ile giri\u015f yapman\u0131z CNN teknolojisi sayesinde m\u00fcmk\u00fcnd\u00fcr.<\/li>\n<li><strong>Adli T\u0131p ve Su\u00e7 Tespiti:<\/strong> G\u00fcvenlik kameralar\u0131ndan elde edilen g\u00f6r\u00fcnt\u00fclerdeki \u015f\u00fcphelileri tan\u0131mlamak i\u00e7in kullan\u0131l\u0131r.<\/li>\n<li><strong>M\u00fc\u015fteri Deneyimi:<\/strong> M\u00fc\u015fterilerin \u00fcr\u00fcn veya hizmetlere verdi\u011fi tepkileri analiz ederek, \u015firketlerin pazarlama stratejilerini iyile\u015ftirmelerine yard\u0131mc\u0131 olabilir.<\/li>\n<\/ul>\n<\/li>\n<li>\n        <strong>G\u00f6r\u00fcnt\u00fc B\u00f6l\u00fctleme (Image Segmentation):<\/strong> G\u00f6r\u00fcnt\u00fcdeki her pikseli belirli bir kategoriye atayarak, nesnelerin tam s\u0131n\u0131rlar\u0131n\u0131 ve \u015fekillerini belirler.<\/p>\n<ul>\n<li><strong>T\u0131bbi G\u00f6r\u00fcnt\u00fcleme:<\/strong> Organlar\u0131n, t\u00fcm\u00f6rlerin veya lezyonlar\u0131n hassas bir \u015fekilde ana hatlar\u0131n\u0131 \u00e7izmek i\u00e7in vazge\u00e7ilmezdir. Bu, do\u011fru tan\u0131 ve tedavi planlamas\u0131 i\u00e7in kritik \u00f6neme sahiptir.<\/li>\n<li><strong>Otonom S\u00fcr\u00fc\u015f:<\/strong> Yoldaki her bir pikselin yol, yaya, ara\u00e7 veya kald\u0131r\u0131m olup olmad\u0131\u011f\u0131n\u0131 belirleyerek arac\u0131n \u00e7evresini detayl\u0131 bir \u015fekilde anlamas\u0131na yard\u0131mc\u0131 olur.<\/li>\n<\/ul>\n<\/li>\n<li>\n        <strong>Stil Transferi ve G\u00f6r\u00fcnt\u00fc \u00dcretimi:<\/strong> Sanatsal stilleri bir g\u00f6r\u00fcnt\u00fcye aktarmak veya tamamen yeni g\u00f6r\u00fcnt\u00fcler olu\u015fturmak i\u00e7in de kullan\u0131labilirler. Bu, \u00f6zellikle sanatsal uygulamalar ve g\u00f6rsel efektler alan\u0131nda pop\u00fclerdir.<\/li>\n<\/ul>\n<p>A\u015fa\u011f\u0131daki tablo, farkl\u0131 CNN uygulamalar\u0131n\u0131 ve bunlar\u0131n g\u00fcnl\u00fck hayattaki kar\u015f\u0131l\u0131klar\u0131n\u0131 \u00f6zetlemektedir:<\/p>\n<table>\n<thead>\n<tr>\n<th>Uygulama Alan\u0131<\/th>\n<th>G\u00f6rev<\/th>\n<th>Ger\u00e7ek D\u00fcnya \u00d6rnekleri<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>G\u00f6r\u00fcnt\u00fc \u0130\u015fleme<\/td>\n<td>S\u0131n\u0131fland\u0131rma, Tan\u0131ma<\/td>\n<td>Otomatik Foto\u011fraf Etiketleme, T\u0131bbi Te\u015fhis (X-ray Analizi)<\/td>\n<\/tr>\n<tr>\n<td>Otonom Sistemler<\/td>\n<td>Nesne Tespiti, B\u00f6l\u00fctleme<\/td>\n<td>S\u00fcr\u00fcc\u00fcs\u00fcz Ara\u00e7lar, Robotik Navigasyon<\/td>\n<\/tr>\n<tr>\n<td>G\u00fcvenlik<\/td>\n<td>Y\u00fcz Tan\u0131ma, Anomali Tespiti<\/td>\n<td>Ak\u0131ll\u0131 Kilitler, Kamu G\u00fcvenli\u011fi \u0130zleme<\/td>\n<\/tr>\n<tr>\n<td>Perakende<\/td>\n<td>Stok Y\u00f6netimi, M\u00fc\u015fteri Analizi<\/td>\n<td>Otomatik Raf \u0130zleme, Kasiyersiz Ma\u011fazalar<\/td>\n<\/tr>\n<tr>\n<td>T\u0131p<\/td>\n<td>Hastal\u0131k Tespiti, Mikroskopik Analiz<\/td>\n<td>Kanser Tarama, H\u00fccre Say\u0131m\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>G\u00f6r\u00fcld\u00fc\u011f\u00fc gibi, Evri\u015fimsel Sinir A\u011flar\u0131, basit bir g\u00f6r\u00fcnt\u00fcy\u00fc anlamadan, karma\u015f\u0131k kararlar alabilen sistemler geli\u015ftirmeye kadar geni\u015f bir yelpazede hayat\u0131m\u0131za de\u011fer katmaktad\u0131r. Bu uygulamalar\u0131n her biri, CNN&#039;lerin \u00f6\u011frenme ve adapte olma yetene\u011finin bir kan\u0131t\u0131d\u0131r ve gelecekte daha da yenilik\u00e7i kullan\u0131m alanlar\u0131yla kar\u015f\u0131la\u015faca\u011f\u0131m\u0131z kesindir.<\/p>\n<h2>Performans\u0131 Art\u0131rmak \u0130\u00e7in \u0130pu\u00e7lar\u0131: Daha Derin Bir Bak\u0131\u015f<\/h2>\n<p>Bir Evri\u015fimsel Sinir A\u011f\u0131&#039;n\u0131 (CNN) sadece tasarlamak yetmez; \u00e7o\u011fu zaman en iyi performans\u0131 elde etmek i\u00e7in \u00e7e\u015fitli optimizasyon ve ince ayar teknikleri uygulamak gerekir. Bu, \u00f6zellikle s\u0131n\u0131rl\u0131 veri setleri veya zorlu g\u00f6revlerle \u00e7al\u0131\u015f\u0131rken kritik \u00f6nem ta\u015f\u0131r. \u0130\u015fte CNN&#039;lerinizin performans\u0131n\u0131 art\u0131rmak i\u00e7in kullanabilece\u011finiz baz\u0131 ileri d\u00fczey ipu\u00e7lar\u0131:<\/p>\n<ul>\n<li>\n        <strong>Transfer \u00d6\u011frenme (Transfer Learning):<\/strong> Belki de en g\u00fc\u00e7l\u00fc ve en s\u0131k kullan\u0131lan tekniklerden biridir. B\u00fcy\u00fck veri setleri \u00fczerinde (\u00f6rne\u011fin, ImageNet gibi binlerce kategori ve milyonlarca g\u00f6r\u00fcnt\u00fc i\u00e7eren) \u00f6nceden e\u011fitilmi\u015f bir CNN modelini (VGG, ResNet, Inception gibi), kendi belirli g\u00f6reviniz i\u00e7in kullanmakt\u0131r. Temel fikir, bu b\u00fcy\u00fck modellerin g\u00f6r\u00fcnt\u00fclerdeki genel \u00f6zellikleri (kenarlar, dokular, \u015fekiller) \u00f6\u011frenmi\u015f olmas\u0131d\u0131r. Siz bu \u00f6nceden e\u011fitilmi\u015f modelin ilk katmanlar\u0131n\u0131 (\u00f6zellik \u00e7\u0131kar\u0131c\u0131 k\u0131sm\u0131) dondurarak veya d\u00fc\u015f\u00fck \u00f6\u011frenme oran\u0131yla yeniden e\u011fiterek, son katmanlar\u0131 (s\u0131n\u0131fland\u0131rma k\u0131sm\u0131) kendi veri setinize g\u00f6re yeniden e\u011fitebilirsiniz. Bu, hem e\u011fitim s\u00fcresini k\u0131salt\u0131r hem de genellikle \u00e7ok daha iyi sonu\u00e7lar verir, \u00f6zellikle kendi veri setiniz k\u00fc\u00e7\u00fckse.<\/p>\n<aside class=\"expert-tip\">\n          Uzman \u0130pucu: Transfer \u00f6\u011frenme ile, s\u0131f\u0131rdan model e\u011fitmek yerine, sekt\u00f6rdeki b\u00fcy\u00fck oyuncular\u0131n geli\u015ftirdi\u011fi ve milyarlarca parametre \u00fczerinde e\u011fitilmi\u015f modellerin g\u00fcc\u00fcnden faydalanarak projelerinizi h\u0131zla ileri ta\u015f\u0131yabilirsiniz. K\u00fc\u00e7\u00fck veri setleri i\u00e7in neredeyse bir zorunluluktur.<br \/>\n        <\/aside>\n<\/li>\n<li>\n        <strong>Veri Art\u0131rma (Data Augmentation):<\/strong> Elinizdeki veri seti s\u0131n\u0131rl\u0131ysa, bu teknik, mevcut g\u00f6r\u00fcnt\u00fcleri hafif\u00e7e de\u011fi\u015ftirerek (d\u00f6nd\u00fcrme, \u00e7evirme, k\u0131rpma, parlakl\u0131k ayar\u0131, yak\u0131nla\u015ft\u0131rma vb.) yeni e\u011fitim \u00f6rnekleri \u00fcretir. Bu, modelin farkl\u0131 varyasyonlara kar\u015f\u0131 daha sa\u011flam olmas\u0131n\u0131 sa\u011flar ve a\u015f\u0131r\u0131 \u00f6\u011frenmeyi (overfitting) azaltmaya yard\u0131mc\u0131 olur. \u00d6rne\u011fin, bir kedinin foto\u011fraf\u0131n\u0131 yatay olarak \u00e7evirerek, modelin hala bir kedi oldu\u011funu tan\u0131mas\u0131n\u0131 sa\u011flam\u0131\u015f olursunuz.<\/p>\n<pre><code class=\"language-python\">\n# Keras ImageDataGenerator ile basit veri art\u0131rma \u00f6rne\u011fi\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\ndatagen = ImageDataGenerator(\n    rotation_range=20,        # Rastgele 20 derece d\u00f6nd\u00fcrme\n    width_shift_range=0.2,    # Geni\u015fli\u011fin %20&#039;si kadar yatay kayd\u0131rma\n    height_shift_range=0.2,   # Y\u00fcksekli\u011fin %20&#039;si kadar dikey kayd\u0131rma\n    horizontal_flip=True,     # Yatay \u00e7evirme\n    brightness_range=[0.8, 1.2] # Parlakl\u0131k de\u011fi\u015fimi\n)\n\n# datagen.fit(x_train) # x_train, e\u011fitim verinizdir\n# Bu \u00fcretici, model.fit() fonksiyonunda kullan\u0131labilir.\n        <\/code><\/pre>\n<\/li>\n<li>\n        <strong>Model Optimizasyonu ve Hiperparametre Ayar\u0131:<\/strong> Bir CNN&#039;in performans\u0131n\u0131 etkileyen bir\u00e7ok &#8220;hiperparametre&#8221; vard\u0131r: \u00f6\u011frenme oran\u0131 (learning rate), batch boyutu, optimizasyon algoritmas\u0131 (Adam, SGD), katman say\u0131s\u0131, filtre say\u0131s\u0131 ve boyutu, dropout oran\u0131 gibi. Bu parametrelerin do\u011fru kombinasyonunu bulmak, genellikle bir deneme-yan\u0131lma s\u00fcrecidir. Grid search, random search veya daha geli\u015fmi\u015f bayesyen optimizasyon gibi teknikler bu s\u00fcreci otomatikle\u015ftirmeye yard\u0131mc\u0131 olabilir. \u00d6\u011frenme oran\u0131n\u0131n do\u011fru ayarlanmas\u0131, modelin h\u0131zl\u0131 ve do\u011fru bir \u015fekilde yak\u0131nsamas\u0131 i\u00e7in \u00f6zellikle kritiktir.<\/li>\n<li>\n        <strong>Regularizasyon Teknikleri (Dropout, L1\/L2 Regularization):<\/strong> A\u015f\u0131r\u0131 \u00f6\u011frenme, derin \u00f6\u011frenme modellerinin yayg\u0131n bir sorunudur. Regularizasyon teknikleri, modelin e\u011fitim verisini ezberlemesini engelleyerek daha iyi genelleme yapmas\u0131n\u0131 sa\u011flar.<\/p>\n<ul>\n<li><strong>Dropout:<\/strong> E\u011fitim s\u0131ras\u0131nda rastgele se\u00e7ilen baz\u0131 n\u00f6ronlar\u0131 ge\u00e7ici olarak devreden \u00e7\u0131kar\u0131r. Bu, modelin belirli n\u00f6ronlara a\u015f\u0131r\u0131 ba\u011f\u0131ml\u0131 olmas\u0131n\u0131 engeller ve daha sa\u011flam \u00f6zellikler \u00f6\u011frenmesini te\u015fvik eder.<\/li>\n<li><strong>L1\/L2 Regularizasyon:<\/strong> Modelin a\u011f\u0131rl\u0131klar\u0131n\u0131n b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc kontrol alt\u0131nda tutarak karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 azalt\u0131r.<\/li>\n<\/ul>\n<\/li>\n<li>\n        <strong>Mimari Se\u00e7imi ve Derinlik:<\/strong> Daha derin a\u011flar genellikle daha karma\u015f\u0131k \u00f6zellikleri \u00f6\u011frenebilir, ancak ayn\u0131 zamanda daha fazla hesaplama g\u00fcc\u00fc gerektirir ve a\u015f\u0131r\u0131 \u00f6\u011frenmeye daha yatk\u0131n olabilirler. G\u00f6revinizin karma\u015f\u0131kl\u0131\u011f\u0131na ve mevcut kaynaklar\u0131n\u0131za uygun bir mimari se\u00e7mek \u00f6nemlidir. Bazen daha basit bir a\u011f, iyi ayarlanm\u0131\u015f hiperparametrelerle \u00e7ok iyi sonu\u00e7lar verebilir. Modern CNN mimarileri (ResNet, Inception, EfficientNet) genellikle bu zorluklar\u0131 a\u015fmak i\u00e7in \u00f6zel tasar\u0131mlar i\u00e7erir.<\/li>\n<\/ul>\n<p>Bu teknikleri bir arada veya ayr\u0131 ayr\u0131 uygulayarak, Evri\u015fimsel Sinir A\u011flar\u0131n\u0131z\u0131n ger\u00e7ek d\u00fcnya senaryolar\u0131nda daha g\u00fcvenilir ve do\u011fru sonu\u00e7lar \u00fcretmesini sa\u011flayabilirsiniz. Unutmay\u0131n, derin \u00f6\u011frenme b\u00fcy\u00fck \u00f6l\u00e7\u00fcde deneysel bir aland\u0131r ve en iyi sonu\u00e7lar genellikle dikkatli denemeler ve iterasyonlar sonucunda elde edilir.<\/p>\n<h2>Mobil Uyumlu CNN Modelleri Olu\u015fturmak M\u00fcmk\u00fcn m\u00fc?<\/h2>\n<p>Ak\u0131ll\u0131 telefonlar\u0131m\u0131z\u0131n ve di\u011fer ta\u015f\u0131nabilir cihazlar\u0131m\u0131z\u0131n i\u015flem g\u00fcc\u00fc her ge\u00e7en g\u00fcn artarken, yapay zeka modellerini do\u011frudan bu cihazlar \u00fczerinde \u00e7al\u0131\u015ft\u0131rma fikri giderek daha cazip hale geliyor. Mobil cihazlar \u00fczerinde \u00e7al\u0131\u015fan CNN&#039;ler, bulut sunucular\u0131na veri g\u00f6nderme ihtiyac\u0131n\u0131 ortadan kald\u0131rarak gecikmeyi azalt\u0131r, gizlili\u011fi art\u0131r\u0131r ve internet ba\u011flant\u0131s\u0131ndan ba\u011f\u0131ms\u0131z \u00e7al\u0131\u015fabilirlik sa\u011flar. Ancak, mobil cihazlar\u0131n k\u0131s\u0131tl\u0131 i\u015flem g\u00fcc\u00fc, bellek ve pil \u00f6mr\u00fc g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, standart b\u00fcy\u00fck CNN modellerini do\u011frudan kullanmak genellikle m\u00fcmk\u00fcn de\u011fildir. Bu noktada &#8220;mobil uyumlu CNN modelleri&#8221; ve ilgili optimizasyon teknikleri devreye girer.<\/p>\n<p>Mobil uyumlu CNN modelleri olu\u015fturman\u0131n ana hedefleri \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>D\u00fc\u015f\u00fck Boyut:<\/strong> Model dosyas\u0131n\u0131n cihazda daha az yer kaplamas\u0131.<\/li>\n<li><strong>D\u00fc\u015f\u00fck Hesaplama Karma\u015f\u0131kl\u0131\u011f\u0131:<\/strong> Daha az i\u015flem g\u00fcc\u00fc t\u00fcketerek daha h\u0131zl\u0131 \u00e7\u0131kar\u0131m yapabilme.<\/li>\n<li><strong>D\u00fc\u015f\u00fck Bellek Kullan\u0131m\u0131:<\/strong> Cihaz\u0131n s\u0131n\u0131rl\u0131 RAM&#039;ini daha verimli kullanma.<\/li>\n<li><strong>D\u00fc\u015f\u00fck G\u00fc\u00e7 T\u00fcketimi:<\/strong> Pil \u00f6mr\u00fcn\u00fc olumsuz etkilemeden \u00e7al\u0131\u015fabilme.<\/li>\n<\/ul>\n<p>Bu hedeflere ula\u015fmak i\u00e7in \u00e7e\u015fitli teknikler kullan\u0131l\u0131r:<\/p>\n<ol>\n<li><strong>Lite Modeller ve \u00d6zel Mimariler:<\/strong> Mobil cihazlar i\u00e7in \u00f6zel olarak tasarlanm\u0131\u015f CNN mimarileri bulunmaktad\u0131r. \u00d6rne\u011fin, Google&#039;\u0131n MobileNet serisi veya SqueezeNet gibi modeller, daha az parametre ve hesaplama gerektiren katman yap\u0131lar\u0131 kullanarak y\u00fcksek do\u011fruluk sa\u011flarken boyutlar\u0131n\u0131 k\u00fc\u00e7\u00fclt\u00fcr. Bu modeller genellikle &#8220;derin ayr\u0131labilir evri\u015fimler&#8221; (depthwise separable convolutions) gibi \u00f6zel teknikler kullan\u0131r.<\/li>\n<li><strong>Model Kuantizasyonu (Quantization):<\/strong> Bu teknik, modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 ve aktivasyonlar\u0131n\u0131 daha d\u00fc\u015f\u00fck bit hassasiyetine d\u00f6n\u00fc\u015ft\u00fcrerek model boyutunu ve hesaplama maliyetini azalt\u0131r. \u00d6rne\u011fin, genellikle 32-bit kayan nokta say\u0131larla temsil edilen a\u011f\u0131rl\u0131klar, 8-bit tam say\u0131lara d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir. Bu, model boyutunu d\u00f6rt kat azaltabilir ve \u00e7o\u011fu durumda performansta kabul edilebilir bir d\u00fc\u015f\u00fc\u015fle gelir.<\/li>\n<li><strong>Model Budama (Pruning):<\/strong> Modeldeki daha az \u00f6nemli olan (yani tahmin \u00fczerinde \u00e7ok az etkisi olan) a\u011f\u0131rl\u0131klar\u0131 veya n\u00f6ronlar\u0131 budama i\u015flemidir. Bu a\u011f\u0131rl\u0131klar s\u0131f\u0131rlan\u0131r ve model inceltilir. Tekrar e\u011fitilerek performans kayb\u0131 minimize edilir.<\/li>\n<li><strong>Bilgi Dam\u0131tma (Knowledge Distillation):<\/strong> B\u00fcy\u00fck, karma\u015f\u0131k bir &#8220;\u00f6\u011fretmen&#8221; modelinden daha k\u00fc\u00e7\u00fck bir &#8220;\u00f6\u011frenci&#8221; modeline bilgi aktar\u0131m\u0131 i\u015flemidir. \u00d6\u011frenci model, \u00f6\u011fretmenin yumu\u015fak etiket tahminlerini taklit etmeyi \u00f6\u011frenir, bu da daha k\u00fc\u00e7\u00fck modelin b\u00fcy\u00fck modelin \u00f6\u011frenme yeteneklerinin bir k\u0131sm\u0131n\u0131 edinmesini sa\u011flar.<\/li>\n<li><strong>Platforma \u00d6zel Optimizasyonlar:<\/strong> TensorFlow Lite (Android ve iOS), Core ML (iOS) veya ONNX Runtime gibi k\u00fct\u00fcphaneler, e\u011fitilmi\u015f CNN modellerini mobil cihazlarda verimli bir \u015fekilde \u00e7al\u0131\u015ft\u0131rmak i\u00e7in optimize edilmi\u015f \u00e7al\u0131\u015fma zaman\u0131 ortamlar\u0131 sunar.<\/li>\n<\/ol>\n<p>CNN modellerinin mobil cihazlarda kullan\u0131m\u0131, kullan\u0131c\u0131 deneyimini zenginle\u015ftiren ve yeni uygulama alanlar\u0131 a\u00e7an b\u00fcy\u00fck bir potansiyele sahiptir. \u00d6rne\u011fin, bir ak\u0131ll\u0131 telefon kameras\u0131n\u0131n anl\u0131k olarak nesneleri tan\u0131mas\u0131 veya bir art\u0131r\u0131lm\u0131\u015f ger\u00e7eklik (AR) uygulamas\u0131n\u0131n ger\u00e7ek d\u00fcnya nesnelerini alg\u0131lamas\u0131, bu optimizasyonlar sayesinde m\u00fcmk\u00fcn olmaktad\u0131r.<\/p>\n<aside class=\"expert-tip\">\n  Uzman \u0130pucu: Mobil cihazlarda CNN sonu\u00e7lar\u0131n\u0131 bir web aray\u00fcz\u00fcnde g\u00f6steriyorsan\u0131z, kullan\u0131c\u0131 deneyimini iyile\u015ftirmek i\u00e7in responsive (duyarl\u0131) tasar\u0131ma sahip olman\u0131z \u015fartt\u0131r. CSS&#039;deki <code>media query<\/code>&#039;ler, ekran boyutuna g\u00f6re d\u00fczeni ayarlaman\u0131za olanak tan\u0131r.<br \/>\n<\/aside>\n<pre><code class=\"language-css\">\n\/* Mobil uyumlu bir web sitesi i\u00e7in basit bir media query \u00f6rne\u011fi *\/\n@media screen and (max-width: 768px) {\n  \/* Ekran geni\u015fli\u011fi 768 pikselden az oldu\u011funda uygulanacak stiller *\/\n  body {\n    font-size: 14px;\n  }\n  .container {\n    width: 95%;\n    padding: 10px;\n  }\n  .sidebar {\n    display: none; \/* Mobil g\u00f6r\u00fcn\u00fcmde kenar \u00e7ubu\u011funu gizle *\/\n  }\n  .main-content {\n    width: 100%; \/* Ana i\u00e7eri\u011fi tam geni\u015flik yap *\/\n  }\n}\n<\/code><\/pre>\n<p>Sonu\u00e7 olarak, Evri\u015fimsel Sinir A\u011flar\u0131 art\u0131k sadece g\u00fc\u00e7l\u00fc sunucularda de\u011fil, cebimizdeki cihazlarda bile ak\u0131ll\u0131 ve etkili bir \u015fekilde \u00e7al\u0131\u015fabiliyor. Bu da yapay zekan\u0131n daha geni\u015f kitlelere ula\u015fmas\u0131n\u0131n ve g\u00fcnl\u00fck hayat\u0131m\u0131z\u0131n ayr\u0131lmaz bir par\u00e7as\u0131 olmas\u0131n\u0131n \u00f6n\u00fcn\u00fc a\u00e7\u0131yor.<\/p>\n<h2>Sonu\u00e7: CNN&#039;lerin Gelece\u011fi ve S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<p>Bu kapsaml\u0131 rehber boyunca, Evri\u015fimsel Sinir A\u011flar\u0131n\u0131n (CNN) ne oldu\u011funu, temel katmanlar\u0131n\u0131 ve \u00e7al\u0131\u015fma prensiplerini ad\u0131m ad\u0131m ke\u015ffettik. G\u00f6r\u00fcnt\u00fcleri anlamak i\u00e7in filtrelerin nas\u0131l kullan\u0131ld\u0131\u011f\u0131ndan, modelin bir \u00f6\u011frenme s\u00fcrecinden nas\u0131l ge\u00e7ti\u011fine ve nihayet ger\u00e7ek d\u00fcnyadaki say\u0131s\u0131z uygulamas\u0131na kadar bir\u00e7ok konuya de\u011findik. CNN&#039;ler, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, nesne tespiti, y\u00fcz tan\u0131ma ve t\u0131bbi te\u015fhis gibi alanlarda devrim yaratarak, bilgisayarlar\u0131n g\u00f6rsel d\u00fcnyay\u0131 insan benzeri bir anlay\u0131\u015fla i\u015flemesinin kap\u0131lar\u0131n\u0131 aralam\u0131\u015ft\u0131r. Ayr\u0131ca, transfer \u00f6\u011frenme, veri art\u0131rma ve mobil optimizasyon gibi tekniklerle modellerin performans\u0131n\u0131 nas\u0131l art\u0131rabilece\u011fimizi de inceledik.<\/p>\n<p>CNN&#039;lerin gelece\u011fi olduk\u00e7a parlak g\u00f6r\u00fcn\u00fcyor. Daha verimli mimariler, daha az veriyle daha iyi \u00f6\u011frenme yetenekleri ve daha g\u00fc\u00e7l\u00fc donan\u0131mlarla birle\u015fti\u011finde, bu teknoloji yeni ufuklar a\u00e7maya devam edecektir. Sa\u011fl\u0131k, otonom sistemler, g\u00fcvenlik, e\u011fitim ve daha bir\u00e7ok sekt\u00f6rde CNN&#039;lerin etkisi derinle\u015fecek ve daha \u00f6nce hayal bile edemedi\u011fimiz yeniliklere yol a\u00e7acakt\u0131r. Yapay zekan\u0131n bu kritik alan\u0131na dair temel bir anlay\u0131\u015f kazanmak, hem teknoloji profesyonelleri hem de merakl\u0131lar i\u00e7in g\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda vazge\u00e7ilmez bir yetenektir.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<ol>\n<li>\n        <strong>CNN&#039;ler sadece g\u00f6r\u00fcnt\u00fcler i\u00e7in mi kullan\u0131l\u0131r?<\/strong><br \/>\n        Hay\u0131r, CNN&#039;ler \u00f6zellikle g\u00f6r\u00fcnt\u00fcler i\u00e7in tasarlanm\u0131\u015f olsa da, metin, ses veya 3D veriler gibi yap\u0131land\u0131r\u0131lm\u0131\u015f di\u011fer veri t\u00fcrlerinde de ba\u015far\u0131yla kullan\u0131ld\u0131\u011f\u0131 uygulamalar mevcuttur. \u00d6rne\u011fin, do\u011fal dil i\u015flemede metin s\u0131n\u0131fland\u0131rmas\u0131 veya genetik verilerin analizi gibi alanlarda da evri\u015fimsel yakla\u015f\u0131mlar adapte edilmi\u015ftir.\n    <\/li>\n<li>\n        <strong>Bir CNN modelini e\u011fitmek ne kadar s\u00fcrer?<\/strong><br \/>\n        E\u011fitim s\u00fcresi, veri setinin boyutu ve karma\u015f\u0131kl\u0131\u011f\u0131na, modelin mimarisine (katman ve parametre say\u0131s\u0131), kullan\u0131lan donan\u0131ma (CPU, GPU, TPU) ve hiperparametre ayarlar\u0131na ba\u011fl\u0131 olarak b\u00fcy\u00fck \u00f6l\u00e7\u00fcde de\u011fi\u015fir. Basit bir model saatler s\u00fcrebilirken, \u00e7ok b\u00fcy\u00fck bir veri seti \u00fczerinde derin bir modelin e\u011fitimi g\u00fcnler, hatta haftalar alabilir.\n    <\/li>\n<li>\n        <strong>Transfer \u00f6\u011frenme her zaman daha iyi sonu\u00e7 verir mi?<\/strong><br \/>\n        \u00c7o\u011fu durumda, \u00f6zellikle kendi veri setiniz k\u00fc\u00e7\u00fck ve \u00f6nceden e\u011fitilmi\u015f modelin e\u011fitimiyle ilgili alan (\u00f6rne\u011fin, genel nesne tan\u0131ma) benzerse, transfer \u00f6\u011frenme \u00e7ok daha iyi sonu\u00e7lar verir. Ancak, e\u011fer g\u00f6reviniz \u00f6nceden e\u011fitilmi\u015f modelin hi\u00e7 g\u00f6rmedi\u011fi veya alakas\u0131z oldu\u011fu bir alan\u0131 kaps\u0131yorsa (\u00f6rne\u011fin, genetik s\u0131ralamadan uzay-zaman s\u00fcreklili\u011fini tahmin etmek), s\u0131f\u0131rdan e\u011fitim veya farkl\u0131 bir model mimarisi daha uygun olabilir.\n    <\/li>\n<li>\n        <strong>CNN&#039;ler neden geleneksel yapay sinir a\u011flar\u0131ndan daha iyidir?<\/strong><br \/>\n        CNN&#039;ler, g\u00f6r\u00fcnt\u00fc verilerinin yerel ba\u011flant\u0131lar\u0131n\u0131 (yani piksellerin birbirine yak\u0131nl\u0131\u011f\u0131n\u0131), a\u011f\u0131rl\u0131k payla\u015f\u0131m\u0131n\u0131 (ayn\u0131 filtrenin g\u00f6r\u00fcnt\u00fcn\u00fcn farkl\u0131 yerlerinde kullan\u0131lmas\u0131n\u0131) ve havuzlama katmanlar\u0131 arac\u0131l\u0131\u011f\u0131yla uzaysal bilgiyi koruma yetenekleri sayesinde geleneksel yapay sinir a\u011flar\u0131ndan \u00fcst\u00fcnd\u00fcr. Geleneksel a\u011flar, g\u00f6r\u00fcnt\u00fcdeki her pikseli ba\u011f\u0131ms\u0131z bir girdi olarak ele al\u0131rken, CNN&#039;ler g\u00f6r\u00fcnt\u00fclerin iki boyutlu (veya \u00e7ok boyutlu) yap\u0131s\u0131ndan faydalan\u0131r, bu da onlar\u0131n g\u00f6r\u00fcnt\u00fcdeki \u00f6r\u00fcnt\u00fcleri \u00e7ok daha verimli bir \u015fekilde \u00f6\u011frenmelerini sa\u011flar.\n    <\/li>\n<\/ol>\n<p><\/code><\/p>\n","protected":false},"excerpt":{"rendered":"Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) Temelleri: Ba\u015flang\u0131\u00e7 Rehberi G\u00fcn\u00fcm\u00fczde yapay zeka ve \u00f6zellikle derin \u00f6\u011frenme, hayat\u0131m\u0131z\u0131n bir\u00e7ok alan\u0131na dokunuyor.&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-34201","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) Nedir ve Neden \u00d6nemlidir?<\/title>\n<meta name=\"description\" content=\"G\u00fcn\u00fcm\u00fczde yapay zeka ve \u00f6zellikle derin \u00f6\u011frenme, hayat\u0131m\u0131z\u0131n bir\u00e7ok alan\u0131na dokunuyor. Peki, bilgisayarlar\u0131n g\u00f6r\u00fcnt\u00fcleri insan gibi anlamas\u0131n\u0131 sa\u011flayan Evri\u015fimsel Sinir A\u011flar\u0131 (CNN&#039;ler) nedir ve nas\u0131l \u00e7al\u0131\u015f\u0131r? Bu rehber, ba\u015flang\u0131\u00e7 seviyesinden karma\u015f\u0131k konulara do\u011fru ad\u0131m ad\u0131m ilerleyerek CNN&#039;lerin b\u00fcy\u00fcl\u00fc d\u00fcnyas\u0131n\u0131 size tan\u0131tacak.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) Nedir ve Neden \u00d6nemlidir?\" \/>\n<meta property=\"og:description\" content=\"G\u00fcn\u00fcm\u00fczde yapay zeka ve \u00f6zellikle derin \u00f6\u011frenme, hayat\u0131m\u0131z\u0131n bir\u00e7ok alan\u0131na dokunuyor. Peki, bilgisayarlar\u0131n g\u00f6r\u00fcnt\u00fcleri insan gibi anlamas\u0131n\u0131 sa\u011flayan Evri\u015fimsel Sinir A\u011flar\u0131 (CNN&#039;ler) nedir ve nas\u0131l \u00e7al\u0131\u015f\u0131r? Bu rehber, ba\u015flang\u0131\u00e7 seviyesinden karma\u015f\u0131k konulara do\u011fru ad\u0131m ad\u0131m ilerleyerek CNN&#039;lerin b\u00fcy\u00fcl\u00fc d\u00fcnyas\u0131n\u0131 size tan\u0131tacak.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-11-12T18:31:25+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"28 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) Nedir ve Neden \u00d6nemlidir?\",\"datePublished\":\"2025-11-12T18:31:25+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/\"},\"wordCount\":5261,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/evrisimsel-sinir-aglari-cnn-nedir-ve-neden-onemlidir\/\",\"name\":\"Evri\u015fimsel Sinir A\u011flar\u0131 (CNN) Nedir ve Neden \u00d6nemlidir?\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-11-12T18:31:25+00:00\",\"description\":\"G\u00fcn\u00fcm\u00fczde yapay zeka ve \u00f6zellikle derin \u00f6\u011frenme, hayat\u0131m\u0131z\u0131n bir\u00e7ok alan\u0131na dokunuyor. 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