{"id":479384,"date":"2023-08-09T10:35:54","date_gmt":"2023-08-09T10:35:54","guid":{"rendered":""},"modified":"2023-09-05T11:18:41","modified_gmt":"2023-09-05T11:18:41","slug":"transfer-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/transfer-learning\/","title":{"rendered":"\u00d6\u011frenimi aktar"},"content":{"rendered":"<p>Transfer \u00d6\u011frenme hakk\u0131nda k\u0131sa bilgi<\/p>\n<p>Transfer \u00f6\u011frenimi, bir g\u00f6rev \u00fczerinde e\u011fitim s\u0131ras\u0131nda kazan\u0131lan bilginin farkl\u0131 fakat ilgili bir probleme uyguland\u0131\u011f\u0131 makine \u00f6\u011frenmesinde (ML) bir ara\u015ft\u0131rma problemidir. Temel olarak transfer \u00f6\u011frenimi, \u00f6nceden e\u011fitilmi\u015f bir modelin yeni bir soruna uyarlanmas\u0131na olanak tan\u0131yarak hesaplama s\u00fcresini ve kaynaklar\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r. \u00d6\u011frenme verimlili\u011fini art\u0131rmaya yard\u0131mc\u0131 olur ve \u00f6zellikle verinin az veya elde edilmesinin pahal\u0131 oldu\u011fu senaryolarda yararl\u0131 olabilir.<\/p>\n<h2>Transfer \u00d6\u011frenmenin K\u00f6keninin Tarihi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Transfer \u00f6\u011frenimi kavram\u0131n\u0131n k\u00f6keni 1900&#039;l\u00fc y\u0131llarda psikoloji alan\u0131na kadar uzanabilir, ancak makine \u00f6\u011frenimi toplulu\u011funda ancak 21. y\u00fczy\u0131l\u0131n ba\u015flar\u0131nda ses getirmeye ba\u015flad\u0131. Caruana&#039;n\u0131n 1997&#039;deki ufuk a\u00e7\u0131c\u0131 \u00e7al\u0131\u015fmas\u0131 &quot;\u00c7oklu G\u00f6rev \u00d6\u011frenme&quot;, bir g\u00f6revden \u00f6\u011frenilen bilginin di\u011ferlerine nas\u0131l uygulanabilece\u011fini anlaman\u0131n temelini att\u0131.<\/p>\n<p>Bu alan, derin \u00f6\u011frenmenin y\u00fckseli\u015fiyle birlikte geli\u015fmeye ba\u015flad\u0131; 2010 y\u0131l\u0131 civar\u0131nda, g\u00f6r\u00fcnt\u00fc tan\u0131ma gibi g\u00f6revlerde \u00f6nceden e\u011fitilmi\u015f sinir a\u011flar\u0131ndan yararlan\u0131lan kayda de\u011fer ilerlemeler kaydedildi.<\/p>\n<h2>Transfer \u00d6\u011frenme Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>Transfer \u00f6\u011frenimi \u00fc\u00e7 ana alana ayr\u0131labilir:<\/p>\n<ol>\n<li><strong>T\u00fcmevar\u0131msal Transfer \u00d6\u011frenme<\/strong>: Baz\u0131 yard\u0131mc\u0131 veriler yard\u0131m\u0131yla hedef tahmin fonksiyonunun \u00f6\u011frenilmesi.<\/li>\n<li><strong>Transd\u00fcktif Transfer \u00d6\u011frenme<\/strong>: Farkl\u0131 fakat ili\u015fkili bir da\u011f\u0131l\u0131m alt\u0131nda hedef tahmin fonksiyonunun \u00f6\u011frenilmesi.<\/li>\n<li><strong>Denetimsiz Transfer \u00d6\u011frenimi<\/strong>: Hem kaynak hem de hedef g\u00f6revlerin denetlenmedi\u011fi yerde \u00f6\u011frenmeyi aktar\u0131n.<\/li>\n<\/ol>\n<p>\u00d6zellikle belirli bir g\u00f6rev i\u00e7in mevcut etiketli verilerin s\u0131n\u0131rl\u0131 oldu\u011fu durumlarda, derin \u00f6\u011frenme modellerini e\u011fitmek i\u00e7in hayati bir teknik haline geldi.<\/p>\n<h2>Transfer \u00d6\u011freniminin \u0130\u00e7 Yap\u0131s\u0131: Transfer \u00d6\u011frenimi Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Transfer \u00f6\u011frenimi, b\u00fcy\u00fck bir veri k\u00fcmesinde \u00f6nceden e\u011fitilmi\u015f bir modelin (bir kaynak) al\u0131nmas\u0131 ve bunun yeni, ilgili bir hedef g\u00f6reve uyarlanmas\u0131yla \u00e7al\u0131\u015f\u0131r. Tipik olarak \u015fu \u015fekilde geli\u015fir:<\/p>\n<ol>\n<li><strong>\u00d6nceden E\u011fitilmi\u015f Bir Modelin Se\u00e7imi<\/strong>: B\u00fcy\u00fck bir veri k\u00fcmesi \u00fczerinde e\u011fitilmi\u015f bir model.<\/li>\n<li><strong>\u0130nce ayar<\/strong>: \u00d6nceden e\u011fitilmi\u015f modelin yeni g\u00f6reve uygun hale getirilmesi i\u00e7in ayarlanmas\u0131.<\/li>\n<li><strong>Yeniden E\u011fitim<\/strong>: De\u011fi\u015ftirilen modelin yeni g\u00f6revle ilgili daha k\u00fc\u00e7\u00fck veri k\u00fcmesi \u00fczerinde e\u011fitilmesi.<\/li>\n<li><strong>De\u011ferlendirme<\/strong>: Performans\u0131 \u00f6l\u00e7mek i\u00e7in yeniden e\u011fitilen modelin yeni g\u00f6rev \u00fczerinde test edilmesi.<\/li>\n<\/ol>\n<h2>Transfer \u00d6\u011frenmenin Temel \u00d6zelliklerinin Analizi<\/h2>\n<ul>\n<li><strong>Yeterlik<\/strong>: E\u011fitim s\u00fcresini \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.<\/li>\n<li><strong>\u00c7ok y\u00f6nl\u00fcl\u00fck<\/strong>: G\u00f6r\u00fcnt\u00fc, metin ve ses dahil \u00e7e\u015fitli alanlara uygulanabilir.<\/li>\n<li><strong>Performans Art\u0131\u015f\u0131<\/strong>: Genellikle yeni g\u00f6rev i\u00e7in s\u0131f\u0131rdan e\u011fitilen modellerden daha iyi performans g\u00f6sterir.<\/li>\n<\/ul>\n<h2>Transfer \u00d6\u011frenme T\u00fcrleri: Tablo ve Listeleri Kullan\u0131n<\/h2>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>end\u00fcktif<\/td>\n<td>Bilgiyi farkl\u0131 ancak ilgili g\u00f6revlere aktar\u0131r<\/td>\n<\/tr>\n<tr>\n<td>\u0130letken<\/td>\n<td>Bilgiyi farkl\u0131 ancak ilgili da\u011f\u0131t\u0131mlar aras\u0131nda aktar\u0131r<\/td>\n<\/tr>\n<tr>\n<td>Denetimsiz<\/td>\n<td>Denetimsiz \u00f6\u011frenme g\u00f6revleri i\u00e7in ge\u00e7erlidir<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Transfer \u00d6\u011frenmeyi Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<ul>\n<li><strong>Farkl\u0131 Alanlarda Kullan\u0131m<\/strong>: G\u00f6r\u00fcnt\u00fc tan\u0131ma, do\u011fal dil i\u015fleme vb.<\/li>\n<li><strong>Zorluklar<\/strong>: \u0130lgili verilerin se\u00e7imi, olumsuz aktar\u0131m riski.<\/li>\n<li><strong>\u00c7\u00f6z\u00fcmler<\/strong>: Kaynak modellerinin dikkatli se\u00e7imi, hiperparametre ayar\u0131.<\/li>\n<\/ul>\n<h2>Tablo ve Listeler \u015eeklinde Ana \u00d6zellikler ve Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>karakteristik<\/th>\n<th>\u00d6\u011frenimi Aktar<\/th>\n<th>Geleneksel \u00d6\u011frenme<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Antrenman vakti<\/td>\n<td>Daha k\u0131sa<\/td>\n<td>Uzun<\/td>\n<\/tr>\n<tr>\n<td>Veri gereksinimleri<\/td>\n<td>Daha az<\/td>\n<td>Daha<\/td>\n<\/tr>\n<tr>\n<td>Esneklik<\/td>\n<td>Y\u00fcksek<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Transfer \u00d6\u011frenimine \u0130li\u015fkin Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Denetimsiz ve kendi kendini denetleyen \u00f6\u011frenmedeki geli\u015fmelerle birlikte transfer \u00f6\u011freniminin b\u00fcy\u00fcmesi bekleniyor. Gelecekteki teknolojiler daha etkili adaptasyon y\u00f6ntemleri, alanlar aras\u0131 uygulamalar ve ger\u00e7ek zamanl\u0131 adaptasyon g\u00f6rebilir.<\/p>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Transfer \u00d6\u011frenimiyle Nas\u0131l \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlara benzer proxy sunucular, b\u00fcy\u00fck veri k\u00fcmeleri olu\u015fturmak i\u00e7in verimli veri kaz\u0131may\u0131 etkinle\u015ftirerek aktar\u0131m \u00f6\u011frenimini kolayla\u015ft\u0131rabilir. G\u00fcvenli ve anonim veri toplama, etik standartlara ve yerel d\u00fczenlemelere uygunlu\u011fu sa\u011flar.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.csd.uwo.ca\/~yuri\/Papers\/tkde.pdf\" target=\"_new\" rel=\"noopener nofollow\">Transfer \u00d6\u011frenme \u00dczerine Bir Ara\u015ft\u0131rma<\/a><\/li>\n<li><a href=\"https:\/\/cs231n.github.io\/transfer-learning\/\" target=\"_new\" rel=\"noopener nofollow\">Stanford&#039;un Transfer \u00d6\u011frenme Kursu<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy: Veri Toplama i\u00e7in Proxy Sunucular\u0131<\/a><\/li>\n<\/ul>","protected":false},"featured_media":470725,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479384","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Transfer Learning<\/mark>","faq_items":[{"question":"What is Transfer Learning?","answer":"<p>Transfer Learning is a technique in machine learning where a model developed for one task is reused as the starting point for a model on a second task. It's about taking a pre-trained model (trained on some large dataset) and fine-tuning it for a new, related problem, thereby saving computation time and resources.<\/p>"},{"question":"How did Transfer Learning originate?","answer":"<p>Transfer Learning can be traced back to the field of psychology in the 1900s, but its application in machine learning began with the work of Caruana in 1997. The growth of deep learning around 2010 further facilitated its widespread adoption in tasks like image recognition.<\/p>"},{"question":"What are the main types of Transfer Learning?","answer":"<p>There are three main types of Transfer Learning: Inductive, where knowledge is transferred across different but related tasks; Transductive, where knowledge is transferred across different but related distributions; and Unsupervised, which applies to unsupervised learning tasks.<\/p>"},{"question":"How does Transfer Learning work?","answer":"<p>Transfer Learning works by taking a pre-trained model on a large dataset and adapting it for a new, related target task. This typically involves selecting a pre-trained model, fine-tuning it, re-training it on the smaller dataset related to the new task, and then evaluating its performance.<\/p>"},{"question":"What are the key features of Transfer Learning?","answer":"<p>The key features of Transfer Learning include its efficiency in reducing training time, versatility across various domains, and often providing a performance boost over models trained from scratch on a new task.<\/p>"},{"question":"What problems might be encountered with Transfer Learning, and how can they be solved?","answer":"<p>Some challenges in Transfer Learning include the selection of relevant data and the risk of negative transfer, where the transfer might hinder instead of help the learning process. These challenges can be overcome by careful selection of source models and proper hyperparameter tuning.<\/p>"},{"question":"How are proxy servers like OneProxy associated with Transfer Learning?","answer":"<p>Proxy servers like those provided by OneProxy can facilitate Transfer Learning by enabling efficient data scraping for building large datasets. This secure and anonymous data collection ensures compliance with ethical standards and local regulations.<\/p>"},{"question":"What are the future perspectives and technologies associated with Transfer Learning?","answer":"<p>Future perspectives related to Transfer Learning include growth in unsupervised and self-supervised learning, more efficient adaptation methods, cross-domain applications, and real-time adaptation.<\/p>"},{"question":"How does Transfer Learning compare to traditional learning methods?","answer":"<p>Compared to traditional learning, Transfer Learning typically requires shorter training time, fewer data requirements, and offers higher flexibility. It can often provide better performance on new tasks compared to models trained from scratch.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479384","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\/479384\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/470725"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=479384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}