{"id":479671,"date":"2023-08-09T10:43:16","date_gmt":"2023-08-09T10:43:16","guid":{"rendered":""},"modified":"2023-09-05T11:19:19","modified_gmt":"2023-09-05T11:19:19","slug":"wide-and-deep-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/wide-and-deep-learning\/","title":{"rendered":"Geni\u015f ve derin \u00f6\u011frenme"},"content":{"rendered":"<p>Geni\u015f ve derin \u00f6\u011frenme, \u00e7ok \u00e7e\u015fitli veri noktalar\u0131ndan verimli bir \u015fekilde \u00f6\u011frenmek ve bunlar\u0131 genelle\u015ftirmek i\u00e7in tasarlanm\u0131\u015f bir makine \u00f6\u011frenimi modelleri s\u0131n\u0131f\u0131d\u0131r. Bu yakla\u015f\u0131m, do\u011frusal modelleri derin \u00f6\u011frenmeyle birle\u015ftirerek hem ezberlemeye hem de genellemeye olanak tan\u0131r.<\/p>\n<h2>Geni\u015f ve Derin \u00d6\u011frenmenin K\u00f6keni ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Geni\u015f ve Derin \u00d6\u011frenme kavram\u0131 ilk olarak 2016 y\u0131l\u0131nda Google ara\u015ft\u0131rmac\u0131lar\u0131 taraf\u0131ndan tan\u0131t\u0131ld\u0131. Buradaki fikir, \u00f6\u011frenmenin iki ana y\u00f6n\u00fc olan ezberleme ve genelleme aras\u0131ndaki bo\u015flu\u011fu kapatmakt\u0131. Ara\u015ft\u0131rmac\u0131lar, do\u011frusal modeller (geni\u015f) ve derin sinir a\u011flar\u0131n\u0131n (derin) bir kombinasyonunu kullanarak \u00f6\u011frenme s\u00fcrecini geli\u015ftirmeyi hedeflediler. Bu, \u00f6zellikle kullan\u0131c\u0131 tercihlerini hat\u0131rlayarak yeni i\u00e7erik \u00f6nermek istedikleri YouTube gibi \u00f6neri sistemlerinde uyguland\u0131.<\/p>\n<h2>Geni\u015f ve Derin \u00d6\u011frenme Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>Geni\u015f ve derin \u00f6\u011frenme, veri kal\u0131plar\u0131n\u0131n genelle\u015ftirilmesine olanak tan\u0131yan bir derin \u00f6\u011frenme modelinin yan\u0131 s\u0131ra, verilerin ezberlenmesine olanak tan\u0131yan geni\u015f bir do\u011frusal modelin kullan\u0131lmas\u0131n\u0131 i\u00e7erir.<\/p>\n<h3>Bile\u015fenler<\/h3>\n<ul>\n<li><strong>Geni\u015f Bile\u015fen<\/strong>: Belirli veri noktalar\u0131n\u0131, korelasyonlar\u0131 ve \u00f6zellikleri ezberlemeye odaklan\u0131r.<\/li>\n<li><strong>Derin Bile\u015fen<\/strong>: Verilerdeki \u00fcst d\u00fczey soyutlamalar\u0131n genelle\u015ftirilmesi ve \u00f6\u011frenilmesi \u00fczerine \u00e7al\u0131\u015f\u0131r.<\/li>\n<\/ul>\n<h3>Uygulamalar<\/h3>\n<ul>\n<li><strong>\u00d6neri Sistemleri<\/strong>: Ki\u015fiselle\u015ftirilmi\u015f \u00f6neriler sunmak.<\/li>\n<li><strong>Arama S\u0131ralamas\u0131<\/strong>: Kullan\u0131c\u0131 kal\u0131plar\u0131n\u0131 anlayarak arama sonu\u00e7lar\u0131n\u0131 geli\u015ftirme.<\/li>\n<li><strong>Tahmine Dayal\u0131 Analitik<\/strong>: Karma\u015f\u0131k tahmin g\u00f6revleri i\u00e7in geni\u015f ve derin modellerin kullan\u0131lmas\u0131.<\/li>\n<\/ul>\n<h2>Geni\u015f ve Derin \u00d6\u011frenmenin \u0130\u00e7 Yap\u0131s\u0131: Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Geni\u015f ve derin bir \u00f6\u011frenme modelinin mimarisi iki ana bile\u015fenden olu\u015fur:<\/p>\n<ol>\n<li><strong>Geni\u015f Bile\u015fen<\/strong>: Giri\u015f \u00f6zelliklerini do\u011frudan \u00e7\u0131k\u0131\u015fa ba\u011flayan do\u011frusal bir model. Bu b\u00f6l\u00fcm, belirli kal\u0131plar\u0131 yakalayan seyrek ve ham girdi \u00f6zellikleriyle ilgilidir.<\/li>\n<li><strong>Derin Bile\u015fen<\/strong>: Birden fazla gizli katmandan olu\u015fan derin bir sinir a\u011f\u0131. Bu b\u00f6l\u00fcm soyut kal\u0131plar\u0131n anla\u015f\u0131lmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n<\/ol>\n<p>Bu bile\u015fenler birlikte ezberlemeyi ve genellemeyi dengeleyen birle\u015fik bir tahmin olu\u015fturur.<\/p>\n<h2>Geni\u015f ve Derin \u00d6\u011frenmenin Temel \u00d6zelliklerinin Analizi<\/h2>\n<ul>\n<li><strong>Esneklik<\/strong>: \u00c7e\u015fitli \u00f6\u011frenme g\u00f6revleri i\u00e7in uygundur.<\/li>\n<li><strong>\u00d6l\u00e7eklenebilirlik<\/strong>: B\u00fcy\u00fck ve karma\u015f\u0131k veri k\u00fcmelerini verimli bir \u015fekilde i\u015fler.<\/li>\n<li><strong>Dengeli \u00d6\u011frenme<\/strong>: Ezberlemenin ve genellemenin avantajlar\u0131n\u0131 birle\u015ftirir.<\/li>\n<li><strong>Geli\u015ftirilmi\u015f Tahmin<\/strong>: Ba\u011f\u0131ms\u0131z modellere g\u00f6re \u00fcst\u00fcn tahmin yetenekleri sunar.<\/li>\n<\/ul>\n<h2>Geni\u015f ve Derin \u00d6\u011frenme T\u00fcrleri<\/h2>\n<p>Geni\u015f ve derin \u00f6\u011frenme modellerinin farkl\u0131 \u00e7e\u015fitleri ve uygulamalar\u0131 bulunmaktad\u0131r. A\u015fa\u011f\u0131da baz\u0131 yayg\u0131n t\u00fcrleri \u00f6zetleyen bir tablo bulunmaktad\u0131r:<\/p>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Geni\u015f Bile\u015fen<\/th>\n<th>Derin Bile\u015fen<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Standart Model<\/td>\n<td>Do\u011frusal Model<\/td>\n<td>Derin Sinir A\u011f\u0131<\/td>\n<\/tr>\n<tr>\n<td>Hibrit Model<\/td>\n<td>\u00d6zelle\u015ftirilmi\u015f Do\u011frusal Model<\/td>\n<td>Evri\u015fimsel Sinir A\u011f\u0131<\/td>\n<\/tr>\n<tr>\n<td>Etki Alan\u0131na \u00d6zel Model<\/td>\n<td>Sekt\u00f6re \u00d6zel Mant\u0131k<\/td>\n<td>Tekrarlayan Sinir A\u011f\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Geni\u015f ve Derin \u00d6\u011frenmeyi Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<h3>Kullan\u0131m<\/h3>\n<ul>\n<li><strong>\u0130\u015f analiti\u011fi<\/strong>: M\u00fc\u015fteri davran\u0131\u015f\u0131n\u0131 tahmin etmek.<\/li>\n<li><strong>Sa\u011fl\u0131k hizmeti<\/strong>: Tedavi planlar\u0131n\u0131n ki\u015fiselle\u015ftirilmesi.<\/li>\n<li><strong>E-Ticaret<\/strong>: \u00dcr\u00fcn \u00f6nerilerinin geli\u015ftirilmesi.<\/li>\n<\/ul>\n<h3>Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h3>\n<ul>\n<li><strong>A\u015f\u0131r\u0131 uyum g\u00f6sterme<\/strong>: Uygun d\u00fczenlemeyle \u00e7\u00f6z\u00fclebilir.<\/li>\n<li><strong>Karma\u015f\u0131kl\u0131k<\/strong>: Model mimarisinin basitle\u015ftirilmesi ve optimizasyonu yard\u0131mc\u0131 olabilir.<\/li>\n<\/ul>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<ul>\n<li><strong>Derin \u00d6\u011frenmeyle kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda<\/strong>: Ezberlemeye daha fazla \u00f6nem verilmesi, belirli ve soyut kal\u0131plar aras\u0131nda denge sa\u011flanmas\u0131.<\/li>\n<li><strong>Do\u011frusal Modellerle Kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda<\/strong>: Derin \u00f6\u011frenmenin kal\u0131plar\u0131 genelle\u015ftirme g\u00fcc\u00fcn\u00fc sunar.<\/li>\n<\/ul>\n<h2>Geni\u015f ve Derin \u00d6\u011frenmeye \u0130li\u015fkin Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Geni\u015f ve derin \u00f6\u011frenmenin gelece\u011fi, a\u015fa\u011f\u0131daki konularda devam eden ara\u015ft\u0131rmalarla umut verici g\u00f6r\u00fcn\u00fcyor:<\/p>\n<ul>\n<li><strong>Otomatik ML<\/strong>: Geni\u015f ve derin modellerin tasar\u0131m\u0131n\u0131n otomatikle\u015ftirilmesi.<\/li>\n<li><strong>\u00d6\u011frenimi Aktar<\/strong>: \u00d6nceden e\u011fitilmi\u015f modellerin \u00e7e\u015fitli alanlara uygulanmas\u0131.<\/li>\n<li><strong>U\u00e7 Bilgi \u0130\u015flem<\/strong>: Ger\u00e7ek zamanl\u0131 analiz i\u00e7in geni\u015f ve derin \u00f6\u011frenmeyi veri kaynaklar\u0131na yakla\u015ft\u0131rma.<\/li>\n<\/ul>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Geni\u015f ve Derin \u00d6\u011frenmeyle Nas\u0131l \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy gibi proxy sunucular geni\u015f ve derin \u00f6\u011frenmede a\u015fa\u011f\u0131daki \u015fekillerde kullan\u0131labilir:<\/p>\n<ul>\n<li><strong>Veri toplama<\/strong>: B\u00fcy\u00fck \u00f6l\u00e7ekli verilerin k\u0131s\u0131tlama olmadan toplanmas\u0131.<\/li>\n<li><strong>Gizlili\u011fin Korunmas\u0131<\/strong>: Modellerin e\u011fitimi s\u0131ras\u0131nda anonimli\u011fin sa\u011flanmas\u0131.<\/li>\n<li><strong>Y\u00fck dengeleme<\/strong>: Da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim s\u0131ras\u0131nda d\u00fc\u011f\u00fcmler aras\u0131ndaki veri aktar\u0131m\u0131n\u0131 verimli bir \u015fekilde y\u00f6netmek.<\/li>\n<\/ul>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1606.07792\" target=\"_new\" rel=\"noopener nofollow\">Geni\u015f ve Derin \u00d6\u011frenmeye \u0130li\u015fkin Google Ara\u015ft\u0131rma Makalesi<\/a><\/li>\n<li><a href=\"https:\/\/www.tensorflow.org\/tutorials\/wide_and_deep\" target=\"_new\" rel=\"noopener nofollow\">TensorFlow Uygulama K\u0131lavuzu<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy Web Sitesi<\/a> Makine \u00f6\u011freniminde proxy sunucu kullan\u0131m\u0131 hakk\u0131nda daha fazla bilgi i\u00e7in.<\/li>\n<\/ul>\n<p>Do\u011frusal modellerin ve derin sinir a\u011flar\u0131n\u0131n g\u00fc\u00e7l\u00fc y\u00f6nlerini birle\u015ftiren geni\u015f ve derin \u00f6\u011frenme, \u00e7e\u015fitli makine \u00f6\u011frenimi zorluklar\u0131na esnek ve g\u00fc\u00e7l\u00fc bir yakla\u015f\u0131m sunar. Proxy sunucular gibi teknolojilerle entegrasyonu, h\u0131zla geli\u015fen yapay zeka alan\u0131nda uygulanabilirli\u011fini ve verimlili\u011fini daha da geni\u015fletiyor.<\/p>","protected":false},"featured_media":470940,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479671","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Wide and Deep Learning<\/mark>","faq_items":[{"question":"What is Wide and Deep Learning?","answer":"<p>Wide and Deep Learning is a machine learning model that combines linear models with deep learning. This combination allows the model to memorize specific data patterns while also generalizing across data, making it effective for various applications like recommendation systems, search ranking, and predictive analytics.<\/p>"},{"question":"When was Wide and Deep Learning first introduced?","answer":"<p>Wide and Deep Learning was first introduced by Google researchers in 2016. The concept was developed to bridge the gap between memorization and generalization in machine learning, and it was initially applied in recommendation systems like YouTube.<\/p>"},{"question":"What are the key components of Wide and Deep Learning?","answer":"<p>The key components of Wide and Deep Learning include the Wide Component, a linear model focusing on memorizing specific data points, and the Deep Component, a deep neural network working on generalizing and learning high-level abstractions in the data.<\/p>"},{"question":"How is Wide and Deep Learning used in recommendation systems?","answer":"<p>In recommendation systems, Wide and Deep Learning helps to recommend new content while remembering user preferences. The wide part memorizes user behavior and specific correlations, while the deep part generalizes this data to recommend content that might align with user interests.<\/p>"},{"question":"What types of Wide and Deep Learning models exist?","answer":"<p>There are different variations of wide and deep learning models, including Standard Models with general linear and deep neural networks, Hybrid Models that can be customized, and Domain-specific Models with industry-specific logic and networks.<\/p>"},{"question":"What are some problems and solutions related to Wide and Deep Learning?","answer":"<p>Some problems include overfitting, which can be addressed by proper regularization, and complexity, which can be alleviated by simplifying and optimizing the model architecture.<\/p>"},{"question":"How are proxy servers like OneProxy associated with Wide and Deep Learning?","answer":"<p>Proxy servers like OneProxy can be utilized in wide and deep learning for purposes such as data collection, privacy preservation, and load balancing. They enable the gathering of large-scale data without restrictions and ensure anonymity while training models.<\/p>"},{"question":"What are the future perspectives related to Wide and Deep Learning?","answer":"<p>The future of wide and deep learning includes ongoing research in areas like AutoML, transfer learning, and edge computing. The integration of these technologies could lead to automating the design of models, applying pre-trained models to various domains, and bringing learning closer to data sources for real-time analytics.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479671","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479671\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/470940"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=479671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}