{"id":475960,"date":"2023-08-09T07:24:43","date_gmt":"2023-08-09T07:24:43","guid":{"rendered":""},"modified":"2023-09-05T11:11:42","modified_gmt":"2023-09-05T11:11:42","slug":"backpropagation","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/backpropagation\/","title":{"rendered":"Geri yay\u0131l\u0131m"},"content":{"rendered":"<p>Geri yay\u0131l\u0131m, yapay sinir a\u011flar\u0131nda (YSA) e\u011fitim ve optimizasyon amac\u0131yla kullan\u0131lan temel bir algoritmad\u0131r. YSA&#039;lar\u0131n verilerden \u00f6\u011frenmesini ve zaman i\u00e7inde performanslar\u0131n\u0131 geli\u015ftirmesini sa\u011flamada hayati bir rol oynar. Geri yay\u0131l\u0131m kavram\u0131, yapay zeka ara\u015ft\u0131rmalar\u0131n\u0131n ilk g\u00fcnlerine kadar uzan\u0131yor ve o zamandan beri modern makine \u00f6\u011frenimi ve derin \u00f6\u011frenme tekniklerinin temel ta\u015f\u0131 haline geldi.<\/p>\n<h2>Geri Yay\u0131l\u0131m\u0131n K\u00f6keninin Tarihi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Geri yay\u0131l\u0131m\u0131n k\u00f6kenleri, ara\u015ft\u0131rmac\u0131lar\u0131n yapay sinir a\u011flar\u0131n\u0131 otomatik olarak e\u011fitmenin yollar\u0131n\u0131 ke\u015ffetmeye ba\u015flad\u0131klar\u0131 1960&#039;lara kadar uzanabilir. 1961&#039;de sinir a\u011flar\u0131n\u0131 geri yay\u0131lmaya benzer bir s\u00fcre\u00e7le e\u011fitmeye y\u00f6nelik ilk giri\u015fim Stuart Dreyfus taraf\u0131ndan doktora derecesinde yap\u0131ld\u0131. tez. Ancak \u201cgeriye yay\u0131l\u0131m\u201d teriminin ilk kez Paul Werbos taraf\u0131ndan YSA&#039;larda \u00f6\u011frenme s\u00fcrecini optimize etmeye y\u00f6nelik \u00e7al\u0131\u015fmas\u0131nda kullan\u0131lmas\u0131 1970&#039;lere kadar de\u011fildi. Geri yay\u0131l\u0131m, 1980&#039;lerde Rumelhart, Hinton ve Williams&#039;\u0131n algoritman\u0131n daha verimli bir versiyonunu tan\u0131tmas\u0131yla b\u00fcy\u00fck ilgi g\u00f6rd\u00fc ve bu da sinir a\u011flar\u0131na olan ilginin yeniden canlanmas\u0131n\u0131 sa\u011flad\u0131.<\/p>\n<h2>Geriye Yay\u0131l\u0131m Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>Geri yay\u0131l\u0131m, \u00f6ncelikle \u00e7ok katmanl\u0131 sinir a\u011flar\u0131n\u0131n e\u011fitimi i\u00e7in kullan\u0131lan denetimli bir \u00f6\u011frenme algoritmas\u0131d\u0131r. Giri\u015f verilerinin a\u011f boyunca ileriye do\u011fru beslenmesi, tahmin edilen \u00e7\u0131kt\u0131 ile ger\u00e7ek \u00e7\u0131kt\u0131 aras\u0131ndaki hatan\u0131n veya kayb\u0131n hesaplanmas\u0131 ve daha sonra bu hatan\u0131n a\u011f\u0131n a\u011f\u0131rl\u0131klar\u0131n\u0131 g\u00fcncellemek i\u00e7in katmanlar boyunca geriye do\u011fru yay\u0131lmas\u0131 gibi yinelemeli bir s\u00fcreci i\u00e7erir. Bu yinelemeli s\u00fcre\u00e7, a\u011f hatan\u0131n en aza indirildi\u011fi ve a\u011f\u0131n yeni giri\u015f verileri i\u00e7in istenen \u00e7\u0131kt\u0131lar\u0131 do\u011fru bir \u015fekilde tahmin edebildi\u011fi bir duruma yakla\u015fana kadar devam eder.<\/p>\n<h2>Geri Yay\u0131l\u0131m\u0131n \u0130\u00e7 Yap\u0131s\u0131: Geri Yay\u0131l\u0131m Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Geri yay\u0131l\u0131m\u0131n i\u00e7 yap\u0131s\u0131 birka\u00e7 temel ad\u0131ma ayr\u0131labilir:<\/p>\n<ol>\n<li>\n<p>\u0130leri Ge\u00e7i\u015f: \u0130leri ge\u00e7i\u015f s\u0131ras\u0131nda, giri\u015f verileri, her katmanda bir dizi a\u011f\u0131rl\u0131kl\u0131 ba\u011flant\u0131 ve aktivasyon fonksiyonu uygulanarak, sinir a\u011f\u0131 \u00fczerinden katman katman beslenir. A\u011f\u0131n \u00e7\u0131k\u0131\u015f\u0131, ba\u015flang\u0131\u00e7 hatas\u0131n\u0131 hesaplamak i\u00e7in temel ger\u00e7ekle kar\u015f\u0131la\u015ft\u0131r\u0131l\u0131r.<\/p>\n<\/li>\n<li>\n<p>Geriye Ge\u00e7i\u015f: Geriye do\u011fru ge\u00e7i\u015fte hata, \u00e7\u0131k\u0131\u015f katman\u0131ndan giri\u015f katman\u0131na do\u011fru geriye do\u011fru yay\u0131l\u0131r. Bu, a\u011fdaki her bir a\u011f\u0131rl\u0131\u011fa g\u00f6re hatan\u0131n gradyanlar\u0131n\u0131 hesaplamak i\u00e7in analizin zincir kural\u0131 uygulanarak elde edilir.<\/p>\n<\/li>\n<li>\n<p>A\u011f\u0131rl\u0131k G\u00fcncellemesi: Gradyanlar elde edildikten sonra a\u011f\u0131n a\u011f\u0131rl\u0131klar\u0131, stokastik gradyan ini\u015f (SGD) veya bunun varyantlar\u0131ndan biri gibi bir optimizasyon algoritmas\u0131 kullan\u0131larak g\u00fcncellenir. Bu g\u00fcncellemeler, a\u011f\u0131n parametrelerini daha iyi tahminler yapacak \u015fekilde ayarlayarak hatay\u0131 en aza indirmeyi ama\u00e7lamaktad\u0131r.<\/p>\n<\/li>\n<li>\n<p>Yinelemeli S\u00fcre\u00e7: \u0130leri ve geri ge\u00e7i\u015fler, belirli say\u0131da d\u00f6nem boyunca veya yak\u0131nsamaya kadar yinelemeli olarak tekrarlan\u0131r, bu da a\u011f\u0131n performans\u0131n\u0131n kademeli olarak iyile\u015ftirilmesine yol a\u00e7ar.<\/p>\n<\/li>\n<\/ol>\n<h2>Geriye Yay\u0131l\u0131m\u0131n Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>Geri yay\u0131l\u0131m, onu sinir a\u011flar\u0131n\u0131n e\u011fitimi i\u00e7in g\u00fc\u00e7l\u00fc bir algoritma haline getiren \u00e7e\u015fitli temel \u00f6zellikler sunar:<\/p>\n<ul>\n<li>\n<p><strong>\u00c7ok y\u00f6nl\u00fcl\u00fck<\/strong>: Geri yay\u0131l\u0131m, ileri beslemeli sinir a\u011flar\u0131, tekrarlayan sinir a\u011flar\u0131 (RNN&#039;ler) ve evri\u015fimli sinir a\u011flar\u0131 (CNN&#039;ler) dahil olmak \u00fczere \u00e7ok \u00e7e\u015fitli sinir a\u011f\u0131 mimarileriyle kullan\u0131labilir.<\/p>\n<\/li>\n<li>\n<p><strong>Yeterlik<\/strong>: Hesaplama a\u00e7\u0131s\u0131ndan yo\u011fun olmas\u0131na ra\u011fmen, geri yay\u0131l\u0131m y\u0131llar i\u00e7inde optimize edilerek b\u00fcy\u00fck veri k\u00fcmelerinin ve karma\u015f\u0131k a\u011flar\u0131n verimli bir \u015fekilde y\u00f6netilmesine olanak sa\u011flanm\u0131\u015ft\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>\u00d6l\u00e7eklenebilirlik<\/strong>: Geri yay\u0131l\u0131m\u0131n paralel do\u011fas\u0131 onu \u00f6l\u00e7eklenebilir hale getirerek modern donan\u0131mlardan ve da\u011f\u0131t\u0131lm\u0131\u015f bilgi i\u015flem kaynaklar\u0131ndan faydalanmas\u0131n\u0131 sa\u011flar.<\/p>\n<\/li>\n<li>\n<p><strong>Do\u011frusal olmama<\/strong>: Geri yay\u0131l\u0131m\u0131n do\u011frusal olmayan etkinle\u015ftirme i\u015flevlerini y\u00f6netme yetene\u011fi, sinir a\u011flar\u0131n\u0131n veriler i\u00e7indeki karma\u015f\u0131k ili\u015fkileri modellemesine olanak tan\u0131r.<\/p>\n<\/li>\n<\/ul>\n<h2>Geriye Yay\u0131l\u0131m T\u00fcrleri<\/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>Standart Geriye Yay\u0131l\u0131m<\/td>\n<td>Her a\u011f\u0131rl\u0131\u011fa g\u00f6re hatan\u0131n tam e\u011fimini kullanarak a\u011f\u0131rl\u0131klar\u0131 g\u00fcncelleyen orijinal algoritma. B\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in hesaplama a\u00e7\u0131s\u0131ndan pahal\u0131 olabilir.<\/td>\n<\/tr>\n<tr>\n<td>Stokastik Geriye Yay\u0131l\u0131m<\/td>\n<td>Her bir veri noktas\u0131ndan sonra a\u011f\u0131rl\u0131klar\u0131 g\u00fcncelleyen standart geri yay\u0131l\u0131m\u0131n optimizasyonu, hesaplama gereksinimlerini azalt\u0131r ancak a\u011f\u0131rl\u0131k g\u00fcncellemelerinde daha fazla rastgelelik sa\u011flar.<\/td>\n<\/tr>\n<tr>\n<td>Mini-toplu Geri Yay\u0131l\u0131m<\/td>\n<td>Standart ve stokastik geriye yay\u0131l\u0131m aras\u0131nda bir uzla\u015fma, veri noktalar\u0131 y\u0131\u011f\u0131nlar\u0131ndaki a\u011f\u0131rl\u0131klar\u0131n g\u00fcncellenmesi. A\u011f\u0131rl\u0131k g\u00fcncellemelerinde hesaplama verimlili\u011fi ile kararl\u0131l\u0131k aras\u0131nda bir denge kurar.<\/td>\n<\/tr>\n<tr>\n<td>Toplu Geri Yay\u0131l\u0131m<\/td>\n<td>A\u011f\u0131rl\u0131klar\u0131 g\u00fcncellemeden \u00f6nce t\u00fcm veri k\u00fcmesinin e\u011fimini hesaplayan alternatif bir yakla\u015f\u0131m. \u00c7o\u011funlukla paralel bilgi i\u015flem ortamlar\u0131nda GPU&#039;lardan veya TPU&#039;lardan verimli bir \u015fekilde yararlanmak i\u00e7in kullan\u0131l\u0131r.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Geri Yay\u0131l\u0131m\u0131 Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<p><strong>Geri Yay\u0131l\u0131m\u0131 Kullanma<\/strong><\/p>\n<ul>\n<li>G\u00f6r\u00fcnt\u00fc Tan\u0131ma: Geri yay\u0131l\u0131m, evri\u015fimli sinir a\u011flar\u0131n\u0131n (CNN&#039;ler) g\u00f6r\u00fcnt\u00fcler i\u00e7indeki nesneleri ve desenleri tan\u0131mlamak \u00fczere e\u011fitildi\u011fi g\u00f6r\u00fcnt\u00fc tan\u0131ma g\u00f6revlerinde yayg\u0131n olarak kullan\u0131l\u0131r.<\/li>\n<li>Do\u011fal Dil \u0130\u015fleme: Geri yay\u0131l\u0131m, dil modelleme, makine \u00e7evirisi ve duygu analizi i\u00e7in tekrarlayan sinir a\u011flar\u0131n\u0131 (RNN&#039;ler) e\u011fitmek amac\u0131yla uygulanabilir.<\/li>\n<li>Finansal Tahmin: Geriye yay\u0131lma, zaman serisi verilerini kullanarak hisse senedi fiyatlar\u0131n\u0131, piyasa e\u011filimlerini ve di\u011fer finansal g\u00f6stergeleri tahmin etmek i\u00e7in kullan\u0131labilir.<\/li>\n<\/ul>\n<p><strong>Zorluklar ve \u00c7\u00f6z\u00fcmler<\/strong><\/p>\n<ul>\n<li><strong>Kaybolan Gradyan Sorunu<\/strong>: Derin sinir a\u011flar\u0131nda, geri yay\u0131l\u0131m s\u0131ras\u0131nda gradyanlar son derece k\u00fc\u00e7\u00fck hale gelebilir, bu da yak\u0131nsaman\u0131n yava\u015flamas\u0131na ve hatta \u00f6\u011frenme s\u00fcrecinin durmas\u0131na neden olabilir. \u00c7\u00f6z\u00fcmler aras\u0131nda ReLU gibi etkinle\u015ftirme i\u015flevlerinin ve toplu normalle\u015ftirme gibi tekniklerin kullan\u0131lmas\u0131 yer al\u0131r.<\/li>\n<li><strong>A\u015f\u0131r\u0131 uyum g\u00f6sterme<\/strong>: Geri yay\u0131l\u0131m, a\u011f\u0131n e\u011fitim verilerinde iyi performans g\u00f6sterdi\u011fi, ancak g\u00f6r\u00fcnmeyen verilerde zay\u0131f performans g\u00f6sterdi\u011fi a\u015f\u0131r\u0131 uyumla sonu\u00e7lanabilir. L1 ve L2 d\u00fczenlemesi gibi d\u00fczenleme teknikleri a\u015f\u0131r\u0131 uyumun azalt\u0131lmas\u0131na yard\u0131mc\u0131 olabilir.<\/li>\n<li><strong>Hesaplama Yo\u011funlu\u011fu<\/strong>: Derin sinir a\u011flar\u0131n\u0131n e\u011fitimi, \u00f6zellikle b\u00fcy\u00fck veri k\u00fcmeleri s\u00f6z konusu oldu\u011funda hesaplama a\u00e7\u0131s\u0131ndan yo\u011fun olabilir. A\u011f mimarisini h\u0131zland\u0131rmak ve optimize etmek i\u00e7in GPU&#039;lar\u0131n veya TPU&#039;lar\u0131n kullan\u0131lmas\u0131 bu sorunu hafifletebilir.<\/li>\n<\/ul>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>karakteristik<\/th>\n<th>Geri yay\u0131l\u0131m<\/th>\n<th>Dereceli al\u00e7alma<\/th>\n<th>Stokastik Gradyan \u0130ni\u015fi<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Tip<\/td>\n<td>Algoritma<\/td>\n<td>Optimizasyon Algoritmas\u0131<\/td>\n<td>Optimizasyon Algoritmas\u0131<\/td>\n<\/tr>\n<tr>\n<td>Ama\u00e7<\/td>\n<td>Sinir A\u011f\u0131 E\u011fitimi<\/td>\n<td>Fonksiyon Optimizasyonu<\/td>\n<td>Fonksiyon Optimizasyonu<\/td>\n<\/tr>\n<tr>\n<td>G\u00fcncelleme s\u0131kl\u0131\u011f\u0131<\/td>\n<td>Her partiden sonra<\/td>\n<td>Her veri noktas\u0131ndan sonra<\/td>\n<td>Her veri noktas\u0131ndan sonra<\/td>\n<\/tr>\n<tr>\n<td>Hesaplama Verimlili\u011fi<\/td>\n<td>Il\u0131man<\/td>\n<td>Y\u00fcksek<\/td>\n<td>Orta ila Y\u00fcksek<\/td>\n<\/tr>\n<tr>\n<td>G\u00fcr\u00fclt\u00fcye Kar\u015f\u0131 Dayan\u0131kl\u0131l\u0131k<\/td>\n<td>Il\u0131man<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>Orta ila D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Geriye Yay\u0131l\u0131mla \u0130lgili Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Geri yay\u0131l\u0131m\u0131n gelece\u011fi donan\u0131m ve algoritmalardaki geli\u015fmelere yak\u0131ndan ba\u011fl\u0131d\u0131r. Hesaplama g\u00fcc\u00fc artmaya devam ettik\u00e7e, daha b\u00fcy\u00fck ve daha karma\u015f\u0131k sinir a\u011flar\u0131n\u0131n e\u011fitimi daha m\u00fcmk\u00fcn hale gelecektir. Ek olarak ara\u015ft\u0131rmac\u0131lar, evrimsel algoritmalar ve biyolojik olarak ilham alan \u00f6\u011frenme y\u00f6ntemleri gibi geleneksel geri yay\u0131l\u0131m\u0131n alternatiflerini aktif olarak ara\u015ft\u0131r\u0131yorlar.<\/p>\n<p>Ayr\u0131ca, d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fcler ve dikkat mekanizmalar\u0131 gibi yeni sinir a\u011f\u0131 mimarileri, do\u011fal dil i\u015fleme g\u00f6revlerinde pop\u00fclerlik kazanm\u0131\u015ft\u0131r ve geri yay\u0131l\u0131m tekniklerinin geli\u015fimini etkileyebilir. Geri yay\u0131l\u0131m\u0131n bu yeni mimarilerle birle\u015fimi muhtemelen \u00e7e\u015fitli alanlarda \u00e7ok daha etkileyici sonu\u00e7lar verecektir.<\/p>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Geriye Yay\u0131l\u0131mla \u0130li\u015fkilendirilebilir?<\/h2>\n<p>Proxy sunucular, \u00f6zellikle b\u00fcy\u00fck \u00f6l\u00e7ekli da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitim ba\u011flam\u0131nda, geri yay\u0131l\u0131m g\u00f6revlerini desteklemede \u00f6nemli bir rol oynayabilir. Derin \u00f6\u011frenme modelleri b\u00fcy\u00fck miktarda veri ve hesaplama g\u00fcc\u00fc gerektirdi\u011finden, ara\u015ft\u0131rmac\u0131lar daha h\u0131zl\u0131 veri al\u0131m\u0131n\u0131 kolayla\u015ft\u0131rmak, kaynaklar\u0131 \u00f6nbelle\u011fe almak ve a\u011f trafi\u011fini optimize etmek i\u00e7in s\u0131kl\u0131kla proxy sunuculardan yararlan\u0131r. Ara\u015ft\u0131rmac\u0131lar, proxy sunucular\u0131 kullanarak veri eri\u015fimini geli\u015ftirebilir ve gecikmeyi en aza indirebilir, b\u00f6ylece sinir a\u011flar\u0131yla daha verimli e\u011fitim ve denemeler yap\u0131labilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2021\/05\/understanding-backpropagation-in-neural-networks\/\" target=\"_new\" rel=\"noopener nofollow\">Sinir A\u011flar\u0131nda Geri Yay\u0131l\u0131m\u0131 Anlamak<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/backpropagation-improving-neural-networks-23b1b3ea4d28\" target=\"_new\" rel=\"noopener nofollow\">Geriye Yay\u0131l\u0131m: Sinir A\u011flar\u0131n\u0131n \u0130yile\u015ftirilmesi<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/gentle-introduction-backpropagation-time\/\" target=\"_new\" rel=\"noopener nofollow\">Geri Yay\u0131lmaya Nazik Bir Giri\u015f<\/a><\/li>\n<li><a href=\"http:\/\/neuralnetworksanddeeplearning.com\/chap2.html\" target=\"_new\" rel=\"noopener nofollow\">Sinir A\u011flar\u0131 ve Derin \u00d6\u011frenme<\/a><\/li>\n<\/ul>","protected":false},"featured_media":475755,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475960","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Backpropagation: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Backpropagation?","answer":"<p>Backpropagation is a fundamental algorithm used in artificial neural networks (ANNs) for training and optimization. It enables ANNs to learn from data and improve their performance over time.<\/p>"},{"question":"How did Backpropagation originate?","answer":"<p>The concept of backpropagation dates back to the 1960s, with early attempts made by Stuart Dreyfus in his Ph.D. thesis. The term \"backpropagation\" was first used by Paul Werbos in the 1970s. It gained significant attention in the 1980s when Rumelhart, Hinton, and Williams introduced a more efficient version of the algorithm.<\/p>"},{"question":"How does Backpropagation work?","answer":"<p>Backpropagation involves a forward pass, where input data is fed through the network, followed by a backward pass, where the error is propagated backward from the output to the input layer. This iterative process updates the network's weights until the error is minimized.<\/p>"},{"question":"What are the key features of Backpropagation?","answer":"<p>Backpropagation is versatile, efficient, scalable, and capable of handling non-linear activation functions. These features make it a powerful algorithm for training neural networks.<\/p>"},{"question":"What types of Backpropagation exist?","answer":"<p>There are several types of backpropagation, including Standard Backpropagation, Stochastic Backpropagation, Mini-batch Backpropagation, and Batch Backpropagation. Each has its advantages and trade-offs.<\/p>"},{"question":"How can Backpropagation be used?","answer":"<p>Backpropagation finds application in various domains, such as image recognition, natural language processing, and financial forecasting.<\/p>"},{"question":"What challenges are associated with Backpropagation, and how can they be solved?","answer":"<p>Backpropagation faces challenges like the vanishing gradient problem and overfitting. Solutions include using activation functions like ReLU, regularization techniques, and optimizing the network architecture.<\/p>"},{"question":"How does Backpropagation compare to Gradient Descent and Stochastic Gradient Descent?","answer":"<p>Backpropagation is an algorithm used in neural network training, while Gradient Descent and Stochastic Gradient Descent are optimization algorithms for function optimization. They differ in update frequency and computational efficiency.<\/p>"},{"question":"What does the future hold for Backpropagation?","answer":"<p>The future of backpropagation lies in advancements in hardware and algorithms, as well as exploring alternatives and combining it with novel neural network architectures.<\/p>"},{"question":"How are Proxy Servers associated with Backpropagation?","answer":"<p>Proxy servers support backpropagation tasks, particularly in large-scale distributed training, by enhancing data access and minimizing latency, leading to more efficient training with neural networks.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/475960","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\/475960\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/475755"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=475960"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}