{"id":478395,"date":"2023-08-09T09:32:22","date_gmt":"2023-08-09T09:32:22","guid":{"rendered":""},"modified":"2023-09-05T11:16:40","modified_gmt":"2023-09-05T11:16:40","slug":"perceptron","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/perceptron\/","title":{"rendered":"Alg\u0131lay\u0131c\u0131"},"content":{"rendered":"<p>Perceptron, makine \u00f6\u011frenimi ve yapay zekada kullan\u0131lan bir t\u00fcr yapay n\u00f6ron veya d\u00fc\u011f\u00fcmd\u00fcr. Biyolojik bir n\u00f6ronun basitle\u015ftirilmi\u015f bir modelini temsil eder ve belirli ikili s\u0131n\u0131fland\u0131r\u0131c\u0131 t\u00fcrleri i\u00e7in temel olu\u015fturur. Girdiyi alarak, toplayarak ve daha sonra onu bir t\u00fcr ad\u0131m fonksiyonundan ge\u00e7irerek \u00e7al\u0131\u015f\u0131r. Perceptron genellikle verileri iki par\u00e7aya ay\u0131rmak i\u00e7in kullan\u0131l\u0131r, bu da onu ikili do\u011frusal s\u0131n\u0131fland\u0131r\u0131c\u0131 yapar.<\/p>\n<h2>Perceptron&#039;un K\u00f6keninin Tarihi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Perceptron, 1957 y\u0131l\u0131nda Cornell Havac\u0131l\u0131k Laboratuvar\u0131&#039;nda Frank Rosenblatt taraf\u0131ndan icat edildi. Ba\u015flang\u0131\u00e7ta insan\u0131n bili\u015fini ve karar verme s\u00fcre\u00e7lerini taklit etme hedefiyle bir donan\u0131m cihaz\u0131 olarak geli\u015ftirildi. Fikir, Warren McCulloch ve Walter Pitts&#039;in 1943&#039;te yapay n\u00f6ronlar \u00fczerine yapt\u0131\u011f\u0131 daha \u00f6nceki \u00e7al\u0131\u015fmalardan esinlenmi\u015fti. Perceptron&#039;un icad\u0131, yapay zekan\u0131n geli\u015fiminde \u00f6nemli bir d\u00f6n\u00fcm noktas\u0131 oldu ve \u00e7evresinden \u00f6\u011frenme yetene\u011fine sahip ilk modeller aras\u0131nda yer ald\u0131.<\/p>\n<h2>Perceptron Hakk\u0131nda Detayl\u0131 Bilgi<\/h2>\n<p>Perceptron, daha karma\u015f\u0131k sinir a\u011flar\u0131n\u0131n i\u015fleyi\u015fini anlamak i\u00e7in kullan\u0131lan basit bir modeldir. Birden fazla ikili girdi al\u0131r ve bunlar\u0131 a\u011f\u0131rl\u0131kl\u0131 bir toplam art\u0131 bir \u00f6nyarg\u0131 yoluyla i\u015fler. \u00c7\u0131kt\u0131 daha sonra aktivasyon fonksiyonu olarak bilinen bir t\u00fcr ad\u0131m fonksiyonundan ge\u00e7irilir.<\/p>\n<h3>Matematiksel G\u00f6sterim:<\/h3>\n<p>Perceptron \u015fu \u015fekilde ifade edilebilir:<\/p>\n<p><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>sen<\/mi><mo>=<\/mo><mi>F<\/mi><mo stretchy=\"false\">(<\/mo><msubsup><mo>\u2211<\/mo><mrow><mi>Ben<\/mi><mo>=<\/mo><mn>1<\/mn><\/mrow><mi>N<\/mi><\/msubsup><msub><mi>w<\/mi><mi>Ben<\/mi><\/msub><msub><mi>X<\/mi><mi>Ben<\/mi><\/msub><mo>+<\/mo><mi>B<\/mi><mo stretchy=\"false\">)<\/mo><\/mrow><annotation encoding=\"application\/x-tex\">y = f(toplam_{i=1}^n w_ix_i + b)<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">sen<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><span class=\"mrel\">=<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.104em; vertical-align: -0.2997em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.10764em;\">F<\/span><span class=\"mopen\">(<\/span><span class=\"mop\"><span class=\"mop op-symbol small-op\" style=\"position: relative; top: 0em;\">\u2211<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8043em;\"><span style=\"top: -2.4003em; margin-left: 0em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\"><span class=\"mord mathnormal mtight\">Ben<\/span><span class=\"mrel mtight\">=<\/span><span class=\"mord mtight\">1<\/span><\/span><\/span><\/span><span style=\"top: -3.2029em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">N<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.2997em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.1667em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.02691em;\">w<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em;\"><span style=\"top: -2.55em; margin-left: -0.0269em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">Ben<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord\"><span class=\"mord mathnormal\">X<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em;\"><span style=\"top: -2.55em; margin-left: 0em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">Ben<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em; vertical-align: -0.25em;\"><\/span><span class=\"mord mathnormal\">B<\/span><span class=\"mclose\">)<\/span><\/span><\/span><\/span><\/span><\/p>\n<p>Neresi <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>sen<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">sen<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">sen<\/span><\/span><\/span><\/span><\/span> \u00e7\u0131kt\u0131d\u0131r, <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><msub><mi>w<\/mi><mi>Ben<\/mi><\/msub><\/mrow><annotation encoding=\"application\/x-tex\">w_i<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.5806em; vertical-align: -0.15em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.02691em;\">w<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em;\"><span style=\"top: -2.55em; margin-left: -0.0269em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">Ben<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> a\u011f\u0131rl\u0131klar, <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><msub><mi>X<\/mi><mi>Ben<\/mi><\/msub><\/mrow><annotation encoding=\"application\/x-tex\">x_i<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.5806em; vertical-align: -0.15em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\">X<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em;\"><span style=\"top: -2.55em; margin-left: 0em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">Ben<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span> girdiler, <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>B<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">B<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6944em;\"><\/span><span class=\"mord mathnormal\">B<\/span><\/span><\/span><\/span><\/span> \u00f6nyarg\u0131d\u0131r ve <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>F<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">F<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.10764em;\">F<\/span><\/span><\/span><\/span><\/span> aktivasyon fonksiyonudur.<\/p>\n<h2>Perceptronun \u0130\u00e7 Yap\u0131s\u0131<\/h2>\n<p>Perceptron a\u015fa\u011f\u0131daki bile\u015fenlerden olu\u015fur:<\/p>\n<ol>\n<li><strong>Giri\u015f Katman\u0131<\/strong>: Giri\u015f sinyallerini al\u0131r.<\/li>\n<li><strong>A\u011f\u0131rl\u0131klar ve \u00d6nyarg\u0131<\/strong>: \u00d6nemli giri\u015fleri vurgulamak i\u00e7in giri\u015f sinyallerine uygulan\u0131r.<\/li>\n<li><strong>Toplama Fonksiyonu<\/strong>: A\u011f\u0131rl\u0131kl\u0131 giri\u015fi ve sapmay\u0131 toplar.<\/li>\n<li><strong>Aktivasyon Fonksiyonu<\/strong>: Birle\u015ftirilmi\u015f toplama g\u00f6re \u00e7\u0131kt\u0131y\u0131 belirler.<\/li>\n<\/ol>\n<h2>Perceptron&#039;un Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>Perceptron&#039;un temel \u00f6zellikleri \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li>Mimarisinde sadelik.<\/li>\n<li>Do\u011frusal olarak ayr\u0131labilen fonksiyonlar\u0131 modelleyebilme.<\/li>\n<li>Giri\u015f \u00f6zelliklerinin \u00f6l\u00e7e\u011fine ve birimlerine duyarl\u0131l\u0131k.<\/li>\n<li>\u00d6\u011frenme oran\u0131n\u0131n se\u00e7imine ba\u011f\u0131ml\u0131l\u0131k.<\/li>\n<li>Do\u011frusal olarak ayr\u0131lamayan problemlerin \u00e7\u00f6z\u00fcm\u00fcnde s\u0131n\u0131rlama.<\/li>\n<\/ul>\n<h2>Perceptron T\u00fcrleri<\/h2>\n<p>Perceptronlar \u00e7e\u015fitli tiplere ayr\u0131labilir. A\u015fa\u011f\u0131da baz\u0131 t\u00fcrleri listeleyen bir tablo bulunmaktad\u0131r:<\/p>\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>Tek katman<\/td>\n<td>Yaln\u0131zca giri\u015f ve \u00e7\u0131k\u0131\u015f katmanlar\u0131ndan olu\u015fur.<\/td>\n<\/tr>\n<tr>\n<td>\u00c7ok katmanl\u0131<\/td>\n<td>Giri\u015f ve \u00e7\u0131k\u0131\u015f katmanlar\u0131 aras\u0131nda gizli katmanlar i\u00e7erir<\/td>\n<\/tr>\n<tr>\n<td>\u00c7ekirdek<\/td>\n<td>Giri\u015f alan\u0131n\u0131 d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in bir \u00e7ekirdek i\u015flevi kullan\u0131r.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perceptron&#039;u Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<p>Alg\u0131lay\u0131c\u0131lar a\u015fa\u011f\u0131dakiler de dahil olmak \u00fczere \u00e7e\u015fitli alanlarda kullan\u0131lmaktad\u0131r:<\/p>\n<ul>\n<li>S\u0131n\u0131fland\u0131rma g\u00f6revleri.<\/li>\n<li>G\u00f6r\u00fcnt\u00fc tan\u0131ma.<\/li>\n<li>Konu\u015fma tan\u0131ma.<\/li>\n<\/ul>\n<h3>Sorunlar:<\/h3>\n<ul>\n<li>Yaln\u0131zca do\u011frusal olarak ayr\u0131labilen fonksiyonlar\u0131 modelleyebilir.<\/li>\n<li>G\u00fcr\u00fclt\u00fcl\u00fc verilere duyarl\u0131d\u0131r.<\/li>\n<\/ul>\n<h3>\u00c7\u00f6z\u00fcmler:<\/h3>\n<ul>\n<li>Do\u011frusal olmayan problemleri \u00e7\u00f6zmek i\u00e7in \u00e7ok katmanl\u0131 bir Perceptron (MLP) kullanma.<\/li>\n<li>G\u00fcr\u00fclt\u00fcy\u00fc azaltmak i\u00e7in verilerin \u00f6n i\u015flenmesi.<\/li>\n<\/ul>\n<h2>Ana \u00d6zellikler ve Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<p>Perceptron&#039;un SVM (Destek Vekt\u00f6r Makinesi) gibi benzer modellerle kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131:<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>Alg\u0131lay\u0131c\u0131<\/th>\n<th>DVM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Karma\u015f\u0131kl\u0131k<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>Orta ila Y\u00fcksek<\/td>\n<\/tr>\n<tr>\n<td>\u0130\u015flevsellik<\/td>\n<td>Do\u011frusal<\/td>\n<td>Do\u011frusal\/Do\u011frusal olmayan<\/td>\n<\/tr>\n<tr>\n<td>Sa\u011flaml\u0131k<\/td>\n<td>Hassas<\/td>\n<td>g\u00fc\u00e7l\u00fc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perceptron ile \u0130lgili Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Gelecek perspektifleri \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li>Kuantum hesaplamayla entegrasyon.<\/li>\n<li>Daha uyarlanabilir \u00f6\u011frenme algoritmalar\u0131 geli\u015ftirmek.<\/li>\n<li>Edge bili\u015fim uygulamalar\u0131 i\u00e7in enerji verimlili\u011finin art\u0131r\u0131lmas\u0131.<\/li>\n<\/ul>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Perceptron ile \u0130li\u015fkilendirilebilir?<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular, Perceptron&#039;lar\u0131n g\u00fcvenli ve verimli e\u011fitimini kolayla\u015ft\u0131rmak i\u00e7in kullan\u0131labilir. Yapabilirler:<\/p>\n<ul>\n<li>E\u011fitim i\u00e7in verilerin g\u00fcvenli aktar\u0131m\u0131n\u0131 etkinle\u015ftirin.<\/li>\n<li>Birden fazla konuma da\u011f\u0131t\u0131lm\u0131\u015f e\u011fitimi kolayla\u015ft\u0131r\u0131n.<\/li>\n<li>Veri \u00f6n i\u015fleme ve d\u00f6n\u00fc\u015ft\u00fcrme verimlili\u011fini art\u0131r\u0131n.<\/li>\n<\/ul>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.link-to-original-paper.com\" target=\"_new\" rel=\"noopener nofollow\">Frank Rosenblatt&#039;\u0131n Perceptron \u00dczerine Orijinal Makalesi<\/a><\/li>\n<li><a href=\"https:\/\/www.neural-networks-introduction.com\" target=\"_new\" rel=\"noopener nofollow\">Sinir A\u011flar\u0131na Giri\u015f<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy Hizmetleri<\/a> geli\u015fmi\u015f proxy \u00e7\u00f6z\u00fcmleri i\u00e7in.<\/li>\n<\/ul>","protected":false},"featured_media":469148,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478395","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Perceptron<\/mark>","faq_items":[{"question":"What is a Perceptron?","answer":"<p>A Perceptron is a type of artificial neuron used in machine learning and artificial intelligence. It is a binary linear classifier that takes multiple inputs, processes them through weighted sums and a bias, and passes the result through an activation function.<\/p>"},{"question":"Who invented the Perceptron, and when was it first developed?","answer":"<p>The Perceptron was invented by Frank Rosenblatt in 1957 at the Cornell Aeronautical Laboratory.<\/p>"},{"question":"What are the main components of the Perceptron?","answer":"<p>The main components of the Perceptron include the Input Layer, Weights and Bias, Summation Function, and Activation Function.<\/p>"},{"question":"What are the key features of the Perceptron?","answer":"<p>The key features of the Perceptron include its simplicity, ability to model linearly separable functions, sensitivity to input scales, and limitation in solving non-linearly separable problems.<\/p>"},{"question":"How can Perceptrons be classified, and what types exist?","answer":"<p>Perceptrons can be classified into Single-Layer, Multilayer, and Kernel types. Single-Layer has only input and output layers, Multilayer contains hidden layers, and Kernel uses a kernel function to transform the input space.<\/p>"},{"question":"What are some problems associated with Perceptrons, and how can they be solved?","answer":"<p>Problems include modeling only linearly separable functions and sensitivity to noisy data. Solutions include utilizing a multilayer Perceptron to solve non-linear problems and preprocessing data to reduce noise.<\/p>"},{"question":"What are the future perspectives and technologies related to Perceptrons?","answer":"<p>Future perspectives include integration with quantum computing, developing more adaptive learning algorithms, and enhancing energy efficiency for edge computing applications.<\/p>"},{"question":"How can proxy servers like OneProxy be used with Perceptrons?","answer":"<p>Proxy servers like OneProxy can be used to facilitate the secure and efficient training of Perceptrons by enabling secure data transfer, facilitating distributed training, and enhancing the efficiency of data preprocessing.<\/p>"},{"question":"Where can I find more information about Perceptrons?","answer":"<p>You can find more information about Perceptrons by visiting resources like <a href=\"https:\/\/www.link-to-original-paper.com\" target=\"_new\">Frank Rosenblatt's Original Paper on Perceptron<\/a> or <a href=\"https:\/\/www.neural-networks-introduction.com\" target=\"_new\">Introduction to Neural Networks<\/a>. For advanced proxy solutions related to Perceptrons, you can visit <a href=\"https:\/\/oneproxy.pro\" target=\"_new\">OneProxy Services<\/a>.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/478395","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\/478395\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/469148"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=478395"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}