{"id":54780,"date":"2020-01-04T00:00:00","date_gmt":"2020-01-03T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/nejroseti-kuda-eto-vse-dvizhetsya"},"modified":"2020-02-18T14:02:50","modified_gmt":"2020-02-18T11:02:50","slug":"nejroseti-kuda-eto-vse-dvizhetsya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/nejroseti-kuda-eto-vse-dvizhetsya","title":{"rendered":"Rrjet\u00eb nervore. Ku po shkon gjith\u00e7ka","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Artikulli ndahet n\u00eb dy pjes\u00eb:<\/p>\n<p><\/p>\n<ol>\n<li>Nj\u00eb p\u00ebrshkrim i shkurt\u00ebr i disa arkitekturave t\u00eb rrjetit p\u00ebr identifikimin e objekteve n\u00eb imazh dhe segmentimin e imazheve me lidhjet m\u00eb t\u00eb qarta q\u00eb njoh. Mundohesha t\u00eb zgjidhja video shpjeguese dhe preferoja ato n\u00eb gjuh\u00ebn shqipe.<\/li>\n<li>Pjesa e dyt\u00eb \u00ebsht\u00eb nj\u00eb p\u00ebrpjekje p\u00ebr t\u00eb kuptuar drejtimin e zhvillimit t\u00eb arkitekturave t\u00eb rrjeteve nervore dhe teknologjive mbi baz\u00ebn e tyre.<\/li>\n<\/ol>\n<p><\/p>\n<p><img decoding=\"async\" alt=\"Rrjet\u00eb nervore. Ku po shkon gjith\u00e7ka\" src=\"\/wp-content\/uploads\/2020\/01\/3e0238547dc956dbb069c11241e4534f.png\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><\/p>\n<p>Figur\u00eb 1 \u2013 Nuk \u00ebsht\u00eb e leht\u00eb t\u00eb kuptosh arkitekturat e rrjeteve nervore<\/p>\n<p><\/p>\n<p>T\u00eb gjitha filluan kur krijova dy aplikacione demonstrative p\u00ebr klasifikimin dhe identifikimin e objekteve n\u00eb telefonin Android:<\/p>\n<p><\/p>\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/foobar167\/junkyard\/tree\/master\/object_classifier\">Demo back-end<\/a><\/noindex>, kur t\u00eb dh\u00ebnat procesohen n\u00eb server dhe d\u00ebrgohen n\u00eb telefon. Klasifikimi i imazheve (image classification) t\u00eb tre llojeve t\u00eb ariut: ari i kalt\u00ebr, ari i zi dhe ari mbush\u00ebs.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/foobar167\/android\/tree\/master\/object_detection_demo\">Demo front-end<\/a><\/noindex>, kur t\u00eb dh\u00ebnat procesohen n\u00eb telefonin e vet. Identifikimi i objekteve (object detection) t\u00eb tre llojeve: filix, fiku dhe datelina.<\/li>\n<\/ul>\n<p><noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<p>Ka nj\u00eb ndryshim midis detyrave t\u00eb klasifikimit t\u00eb imazheve, identifikimit t\u00eb objekteve n\u00eb imazh dhe <noindex><a rel=\"nofollow\" href=\"https:\/\/medium.com\/analytics-vidhya\/image-classification-vs-object-detection-vs-image-segmentation-f36db85fe81\">segmentimit t\u00eb imazheve<\/a><\/noindex>. Prandaj, u shfaq nevoja p\u00ebr t\u00eb kuptuar cilat arkitektura t\u00eb rrjeteve nervore zbulonin objekte n\u00eb imazhe dhe cilat mund t\u00eb segmentejn\u00eb ato. Gjeta k\u00ebto shembuj arkitekturash me lidhje m\u00eb t\u00eb qarta p\u00ebr mua:<\/p>\n<p><\/p>\n<ul>\n<li>Seria e arkitekturave t\u00eb bazuara n\u00eb R-CNN (<strong>R<\/strong>regjione me <strong>C<\/strong>konvolucion <strong>N<\/strong>rrjete nervore): R-CNN, Fast R-CNN, <strong>N<\/strong>Faster R-CNN <noindex><a rel=\"nofollow\" href=\"https:\/\/medium.com\/@smallfishbigsea\/faster-r-cnn-explained-864d4fb7e3f8\">Mask R-CNN<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/0vt05rQqk_I\">. P\u00ebr zbulimin e objekteve n\u00eb imazhe me mekanizmin Region Proposal Network (RPN), caktohen rajone t\u00eb kufizuara (bounding boxes). Fillimisht, p\u00ebrpara RPN, u p\u00ebrdor nj\u00eb mekaniz\u00ebm m\u00eb t\u00eb ngadalsh\u00ebm, Selective Search. Pastaj, rajonet e kufizuara t\u00eb theksuara jepen si input p\u00ebr nj\u00eb rrjet nervor t\u00eb zakonsh\u00ebm p\u00ebr klasifikim. N\u00eb arkitektur\u00ebn R-CNN ka cikle t\u00eb qarta 'for' p\u00ebr t\u00eb kaluar n\u00ebp\u00ebr rajonet e kufizuara, deri n\u00eb 2000 kalime p\u00ebrmes rrjetit t\u00eb brendsh\u00ebm AlexNet. P\u00ebr shkak t\u00eb cikleve t\u00eb qarta 'for', ngadal\u00ebsohet shpejt\u00ebsia e p\u00ebrpunimit t\u00eb imazheve. Numri i cikleve t\u00eb qarta, kalimeve p\u00ebrmes rrjetit t\u00eb brendsh\u00ebm, zvog\u00eblohet me \u00e7do version t\u00eb ri t\u00eb arkitektur\u00ebs, si dhe kryhen dhjet\u00ebra ndryshime t\u00eb tjera p\u00ebr t\u00eb rritur shpejt\u00ebsin\u00eb dhe p\u00ebr t\u00eb z\u00ebvend\u00ebsuar detyr\u00ebn e zbulimit t\u00eb objekteve me segmentimin e objekteve n\u00eb Mask R-CNN.<\/a><\/noindex>. \u0414\u043b\u044f \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u043e\u0431\u044a\u0435\u043a\u0442\u0430 \u043d\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043c\u0435\u0445\u0430\u043d\u0438\u0437\u043c\u0430 Region Proposal Network (RPN) \u0432\u044b\u0434\u0435\u043b\u044f\u044e\u0442\u0441\u044f \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u043d\u044b\u0435 \u0440\u0435\u0433\u0438\u043e\u043d\u044b (bounding boxes). \u041f\u0435\u0440\u0432\u043e\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e \u0432\u043c\u0435\u0441\u0442\u043e RPN \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u043b\u0441\u044f \u0431\u043e\u043b\u0435\u0435 \u043c\u0435\u0434\u043b\u0435\u043d\u043d\u044b\u0439 \u043c\u0435\u0445\u0430\u043d\u0438\u0437\u043c Selective Search. \u0417\u0430\u0442\u0435\u043c \u0432\u044b\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0435 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u043d\u044b\u0435 \u0440\u0435\u0433\u0438\u043e\u043d\u044b \u043f\u043e\u0434\u0430\u044e\u0442\u0441\u044f \u043d\u0430 \u0432\u0445\u043e\u0434 \u043e\u0431\u044b\u0447\u043d\u043e\u0439 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u0434\u043b\u044f \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438. \u0412 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0435 R-CNN \u0435\u0441\u0442\u044c \u044f\u0432\u043d\u044b\u0435 \u0446\u0438\u043a\u043b\u044b \u00abfor\u00bb \u043f\u0435\u0440\u0435\u0431\u043e\u0440\u0430 \u043f\u043e \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u043d\u044b\u043c \u0440\u0435\u0433\u0438\u043e\u043d\u0430\u043c, \u0432\u0441\u0435\u0433\u043e \u0434\u043e 2000 \u043f\u0440\u043e\u0433\u043e\u043d\u043e\u0432 \u0447\u0435\u0440\u0435\u0437 \u0432\u043d\u0443\u0442\u0440\u0435\u043d\u043d\u044e\u044e \u0441\u0435\u0442\u044c AlexNet. \u0418\u0437-\u0437\u0430 \u044f\u0432\u043d\u044b\u0445 \u0446\u0438\u043a\u043b\u043e\u0432 \u00abfor\u00bb \u0437\u0430\u043c\u0435\u0434\u043b\u044f\u0435\u0442\u0441\u044f \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u044c \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439. \u041a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u044f\u0432\u043d\u044b\u0445 \u0446\u0438\u043a\u043b\u043e\u0432, \u043f\u0440\u043e\u0433\u043e\u043d\u043e\u0432 \u0447\u0435\u0440\u0435\u0437 \u0432\u043d\u0443\u0442\u0440\u0435\u043d\u043d\u044e\u044e \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u044c, \u0443\u043c\u0435\u043d\u044c\u0448\u0430\u0435\u0442\u0441\u044f \u0441 \u043a\u0430\u0436\u0434\u043e\u0439 \u043d\u043e\u0432\u043e\u0439 \u0432\u0435\u0440\u0441\u0438\u0435\u0439 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u044b, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043f\u0440\u043e\u0432\u043e\u0434\u044f\u0442\u0441\u044f \u0434\u0435\u0441\u044f\u0442\u043a\u0438 \u0434\u0440\u0443\u0433\u0438\u0445 \u0438\u0437\u043c\u0435\u043d\u0435\u043d\u0438\u0439 \u0434\u043b\u044f \u0443\u0432\u0435\u043b\u0438\u0447\u0435\u043d\u0438\u044f \u0441\u043a\u043e\u0440\u043e\u0441\u0442\u0438 \u0438 \u0434\u043b\u044f \u0437\u0430\u043c\u0435\u043d\u044b \u0437\u0430\u0434\u0430\u0447\u0438 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u044f \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432 \u043d\u0430 \u0441\u0435\u0433\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432 \u0432 Mask R-CNN.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/L0tzmv--CGY\">YOLO<\/a><\/noindex> (<strong>Y<\/strong>q\u00eb <strong>O<\/strong>duhet <strong>L<\/strong>t\u00eb <strong>O<\/strong>nce) \u2013 rrjeta e par\u00eb nervore q\u00eb njohu objekte n\u00eb koh\u00eb reale n\u00eb pajisjet celulare. Karakteristika dalluese: dallimi i objekteve pas nj\u00eb kalimi (mjafton t\u00eb shikosh nj\u00eb her\u00eb). Pra, n\u00eb arkitektur\u00ebn YOLO nuk ka cikle t\u00eb qarta \"for\", p\u00ebr shkak t\u00eb s\u00eb cil\u00ebs rrjeta punon shpejt. P\u00ebr shembull, \u00ebsht\u00eb si analogjia q\u00eb n\u00eb NumPy, gjat\u00eb operacioneve me matrica, gjithashtu nuk ka cikle t\u00eb qarta \"for\", t\u00eb cilat n\u00eb NumPy realizohen n\u00eb nivele m\u00eb t\u00eb ulta arkitekture p\u00ebrmes gjuh\u00ebs s\u00eb programimit C. YOLO p\u00ebrdor nj\u00eb gril\u00eb me dritare t\u00eb paracaktuara. P\u00ebr t\u00eb shmangur identifikimin e t\u00eb nj\u00ebjtit objekt disa her\u00eb, p\u00ebrdoret coefficienti i mbivendosur t\u00eb dritareve (IoU, <strong>I<\/strong>ntersection <strong>o<\/strong>ver <strong>U<\/strong>nion). Kjo arkitektur\u00eb punon n\u00eb nj\u00eb gam\u00eb t\u00eb gjer\u00eb dhe ka <noindex><a rel=\"nofollow\" href=\"https:\/\/ru.wikipedia.org\/wiki\/%D0%A0%D0%BE%D0%B1%D0%B0%D1%81%D1%82%D0%BD%D0%BE%D1%81%D1%82%D1%8C\">robust\u00ebsi<\/a><\/noindex>: modeli mund t\u00eb trajnohet n\u00eb fotografi, por megjithat\u00eb punon mir\u00eb n\u00eb piktura t\u00eb vizatuara.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/P8e-G-Mhx4k\">SSD<\/a><\/noindex> (<strong>S<\/strong>ingle <strong>S<\/strong>hot MultiBox <strong>D<\/strong>P\u00ebrdoren trikimet m\u00eb t\u00eb dobishme t\u00eb arkitektur\u00ebs YOLO (p\u00ebr shembull, non-maximum suppression) dhe shtohen t\u00eb reja, p\u00ebr t\u00eb punuar m\u00eb shpejt dhe sakt\u00eb me nevrale. Karakteristika dalluese: dallimi i objekteve n\u00eb nj\u00eb kalim me an\u00eb t\u00eb nj\u00eb rrjeti dritash (default box) n\u00eb piramid\u00ebn e imazheve. Piramida e imazheve \u00ebsht\u00eb koduar n\u00eb tenzor\u00ebt konvulutes duke kryer operacione t\u00eb renditjes dhe pooling (n\u00eb operacionin max-pooling, dimensioni hap\u00ebsinor zvog\u00eblohet). K\u00ebshtu p\u00ebrcaktohen si objektet e m\u00ebdha ashtu edhe ato t\u00eb vogla n\u00eb nj\u00eb kalim t\u00eb rrjetit.<\/li>\n<li>MobileSSD (<strong>Mobile<\/strong>NetV2 + <strong>SSD<\/strong>) \u2013 kombinimi i dy arkitekturave t\u00eb rrjeteve nervore. Rrjeti i par\u00eb <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/352804\/\">MobileNetV2<\/a><\/noindex> punon shpejt dhe rrit sakt\u00ebsin\u00eb e njohjes. MobileNetV2 p\u00ebrdoret n\u00eb vend t\u00eb VGG-16, e cila u p\u00ebrdor fillimisht n\u00eb <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1512.02325\">artikulli origjinal<\/a><\/noindex>. Rrjeti i dyt\u00eb SSD p\u00ebrcakton pozitat e objekteve n\u00eb imazh.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/ge_RT5wvHvY\">SqueezeNet<\/a><\/noindex> \u2013 nj\u00eb rrjet neural shum\u00eb t\u00eb vog\u00ebl, por t\u00eb sakt\u00eb. Ai vet\u00eb nuk zgjidh problemin e objektit. Megjithat\u00eb, mund t\u00eb p\u00ebrdoret n\u00eb kombinim me arkitektura t\u00eb ndryshme. Dhe mund t\u00eb p\u00ebrdoret n\u00eb pajisje mobile. Karakteristika dalluese \u00ebsht\u00eb se fillimisht t\u00eb dh\u00ebnat kompresohen n\u00eb kat\u00ebr filtra konvolucion 1\u00d71, dhe pastaj zgjerohen n\u00eb kat\u00ebr filtra konvolucion 1\u00d71 dhe kat\u00ebr filtra konvolucion 3\u00d73. Nj\u00eb iteracion i till\u00eb i kompresimit dhe zgjerimit t\u00eb t\u00eb dh\u00ebnave quhet \u00abModuli i Zjarrit\u00bb.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/b6jhopSMit8\">DeepLab<\/a><\/noindex> (Segmentimi i Imazheve Semantike me Rrjeta Konvencionale t\u00eb thella) \u2013 segmentimi i objekteve n\u00eb nj\u00eb imazh. Karakteristika dalluese e arkitektur\u00ebs \u00ebsht\u00eb konvolucioni i holluar (dilated convolution), i cili ruan rezolut\u00ebn hap\u00ebsinore. Pas k\u00ebsaj, ndodh faza e p\u00ebrpunimit post-ekzekutiv t\u00eb rezultateve duke p\u00ebrdorur nj\u00eb model probabilistik grafik (conditional random field), q\u00eb lejon heqjen e zhurmave t\u00eb vogla n\u00eb segmentim dhe p\u00ebrmir\u00ebsimin e cil\u00ebsis\u00eb s\u00eb imazhit t\u00eb segmentuar. Pas emrit t\u00eb fuqish\u00ebm \u00abmodel probabilistik grafik\u00bb fshihet nj\u00eb filt\u00ebr i zakonsh\u00ebm Gauss, i cili \u00ebsht\u00eb aproksimuar n\u00eb baz\u00eb t\u00eb pes\u00eb pikave.<\/li>\n<li>P\u00ebrpiqesha t\u00eb kuptoja mekanizmin <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1711.06897\">RefineDet<\/a><\/noindex> (Rrjet Neural i Q\u00ebndruesh\u00ebm p\u00ebr Objektin n\u00eb nj\u00eb Gjuajtje <strong>Refine<\/strong>ment Neural Network for Object <strong>Det<\/strong>m\u00ebnyr\u00ebs, por nuk kuptova shum\u00eb.<\/li>\n<li>Shikova gjithashtu se si funksionon teknologjia \"v\u00ebmendje\" (attention): <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/W2rWgXJBZhU\">video1<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/iDulhoQ2pro\">video2<\/a><\/noindex>, <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/H6Qiegq_36c\">video3<\/a><\/noindex>. Nj\u00eb karakteristik\u00eb e ve\u00e7ant\u00eb e arkitektur\u00ebs \"v\u00ebmendje\" \u00ebsht\u00eb nxjerrja automatikisht e rajoneve me v\u00ebmendje t\u00eb p\u00ebrmir\u00ebsuar n\u00eb imazh (RoI, <strong>R<\/strong>Rajonet <strong>o<\/strong>f <strong>I<\/strong>e Interesit) p\u00ebrmes nj\u00eb rrjete nervore t\u00eb quajtur Nj\u00ebsia e V\u00ebmendjes. Rajonet me v\u00ebmendje t\u00eb p\u00ebrmir\u00ebsuar ngjasojn\u00eb me kutit\u00eb kufizuese (bounding boxes), por ndryshe nga ato, nuk jan\u00eb t\u00eb ngulitura n\u00eb imazh dhe mund t\u00eb ken\u00eb kufij t\u00eb paq\u00ebndruesh\u00ebm. M\u00eb pas, nga rajonet me v\u00ebmendje t\u00eb p\u00ebrmir\u00ebsuar nxirren tiparet (features), t\u00eb cilat \"ushqeh\u00ebn\" n\u00eb rrjetet nervore rekurent\u00eb me arkitektura <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/5lUUrREboSk\">LSTM, GRU ose Vanilla RNN<\/a><\/noindex>. Rrjetet nervore rekurent\u00eb jan\u00eb t\u00eb afta t\u00eb analizojn\u00eb marr\u00ebdh\u00ebnien e tipareve n\u00eb nj\u00eb sekuenc\u00eb. Rrjetet nervore rekurent\u00eb fillimisht u p\u00ebrdor\u00ebn p\u00ebr p\u00ebrkthimin e teksteve n\u00eb gjuh\u00eb t\u00eb tjera, tani edhe p\u00ebr p\u00ebrkthimin <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/e-WB4lfg30M\">nga imazhi n\u00eb tekst<\/a><\/noindex> dhe <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/rAbhypxs1qQ\">nga teksti n\u00eb imazh<\/a><\/noindex>.<\/li>\n<\/ul>\n<p><\/p>\n<p>Me kalimin e koh\u00ebs duke studiuar k\u00ebto arkitektura <strong>kuptova se nuk kuptoj asgj\u00eb.<\/strong>. Dhe nuk \u00ebsht\u00eb se n\u00eb rrjetin tim nervor ka probleme me mekanizmin e v\u00ebmendjes. Krijimi i t\u00eb gjitha k\u00ebtyre arkitekturave \u00ebsht\u00eb si nj\u00eb hackathon i madh, ku autor\u00ebt garojn\u00eb n\u00eb hacks. Hack (hack) \u00ebsht\u00eb nj\u00eb zgjidhje e shpejt\u00eb p\u00ebr nj\u00eb detyr\u00eb t\u00eb v\u00ebshtir\u00eb programore. Pra, mes t\u00eb gjitha k\u00ebtyre arkitekturave nuk ka nj\u00eb lidhje t\u00eb dukshme dhe logjike. E vetmja gj\u00eb q\u00eb i bashkon ato \u00ebsht\u00eb nj\u00eb grup hacks t\u00eb suksessh\u00ebm q\u00eb ata huazojn\u00eb nga nj\u00ebri-tjetri, plus nj\u00eb lidhje e p\u00ebrbashk\u00ebt p\u00ebr t\u00eb gjith\u00eb <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/Ilg3gGewQ5U\">operacioni i konvolucionit me feedback<\/a><\/noindex> (p\u00ebrhapja e prapme e gabimeve, backpropagation). Jo <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/272473\/\">mendimi sistemor<\/a><\/noindex>! Nuk \u00ebsht\u00eb e qart\u00eb se \u00e7far\u00eb duhet t\u00eb ndryshohet dhe si t\u00eb optimizohen arritjet ekzistuese.<\/p>\n<p><\/p>\n<p>Si rezultat i munges\u00ebs s\u00eb lidhjeve logjike mes hacks, ato jan\u00eb tep\u00ebr t\u00eb v\u00ebshtira p\u00ebr t'u mbajtur mend dhe p\u00ebr t'u aplikuar n\u00eb praktik\u00eb. K\u00ebto jan\u00eb njohuri fragmentarike. N\u00eb rastin m\u00eb t\u00eb mir\u00eb, disa momente interesante dhe t\u00eb papritura mbahen mend, por shumica e asaj q\u00eb u kuptua dhe nuk u kuptua zhduket nga memoria pas disa dit\u00ebsh. Do t\u00eb ishte mir\u00eb n\u00ebse pas nj\u00eb jav\u00eb do t\u00eb kujtohej t\u00eb pakt\u00ebn emri i arkitektur\u00ebs. Dhe p\u00ebr t\u00eb lexuar artikuj dhe p\u00ebr t\u00eb par\u00eb video p\u00ebrmbledh\u00ebse u shpenzuan disa or\u00eb dhe madje dit\u00eb t\u00eb koh\u00ebs s\u00eb pun\u00ebs!<\/p>\n<p><\/p>\n<p><img decoding=\"async\" alt=\"Rrjet\u00eb nervore. Ku po shkon gjith\u00e7ka\" src=\"\/wp-content\/uploads\/2020\/01\/86f0f24e3be1d0f1a210f3132897d981.png\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><\/p>\n<p>Figura 2 \u2013 <noindex><a rel=\"nofollow\" href=\"https:\/\/www.asimovinstitute.org\/neural-network-zoo\/\">Zoologjia e rrjeteve nervore<\/a><\/noindex><\/p>\n<p><\/p>\n<p>Sip\u00ebrfaqja e artikujve shkencor\u00eb, sipas mendimit tim personal, \u00ebsht\u00eb se shumica e autor\u00ebve p\u00ebrpiqen t\u00eb b\u00ebjn\u00eb t\u00eb vet\u00ebm q\u00eb edhe k\u00ebto njohuri t\u00eb fragmentuara t\u00eb mos kuptohen nga lexuesi. Por, nd\u00ebrtimet e pjes\u00ebve t\u00eb folura n\u00eb dhjet\u00eb proza me formula, t\u00eb marra 'nga tavani' \u2013 ky \u00ebsht\u00eb nj\u00eb tem\u00eb p\u00ebr nj\u00eb artikull t\u00eb ve\u00e7ant\u00eb (problemi <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Publish_or_perish\">publish or perish<\/a><\/noindex>).<\/p>\n<p><\/p>\n<p>P\u00ebr k\u00ebt\u00eb arsye, ka lindur nevoja p\u00ebr t\u00eb sistematizuar informacionin n\u00eb lidhje me rrjetet nervore dhe, n\u00eb k\u00ebt\u00eb m\u00ebnyr\u00eb, p\u00ebr t\u00eb rritur cil\u00ebsin\u00eb e kuptimit dhe memorizimit. K\u00ebshtu, tema kryesore e shqyrtimit t\u00eb teknologjive dhe arkitekturave t\u00eb ndryshme t\u00eb rrjeteve nervore artificiale ishte detyra e m\u00ebposhtme: <strong>t\u00eb kuptojm\u00eb se ku po shkon gjith\u00e7ka<\/strong>, dhe jo struktura e ndonj\u00eb rrjeti nervor t\u00eb ve\u00e7ant\u00eb.<\/p>\n<p><\/p>\n<p>Ku po shkon gjith\u00e7ka. Rezultatet kryesore:<\/p>\n<p><\/p>\n<ul>\n<li>Numri i startupeve n\u00eb fush\u00ebn e m\u00ebsimit mb\u00ebshtet\u00ebs n\u00eb dy vitet e fundit <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/recognitor\/blog\/455676\/\">ka r\u00ebn\u00eb ndjesh\u00ebm<\/a><\/noindex>. Shkaku i mundsh\u00ebm: 'rrjetet nervore kan\u00eb ndaluar s\u00eb qeni di\u00e7ka e re'.<\/li>\n<li>\u00c7do kush mund t\u00eb krijoj\u00eb nj\u00eb rrjet nervor funksional p\u00ebr t\u00eb zgjidhur nj\u00eb detyr\u00eb t\u00eb thjesht\u00eb. P\u00ebr k\u00ebt\u00eb, do t\u00eb marr\u00eb nj\u00eb model t\u00eb gatsh\u00ebm nga 'zoo e modeleve' (model zoo) dhe do t\u00eb st\u00ebrvis\u00eb shtres\u00ebn e fundit t\u00eb rrjetit nervor (<noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/yofjFQddwHE\">transfer learning<\/a><\/noindex>) mbi t\u00eb dh\u00ebnat e gatshme nga <noindex><a rel=\"nofollow\" href=\"https:\/\/toolbox.google.com\/datasetsearch\">K\u00ebrkimi n\u00eb Dataset-in e Google<\/a><\/noindex> apo nga <noindex><a rel=\"nofollow\" href=\"https:\/\/www.kaggle.com\/datasets\">25,000 datasets n\u00eb Kaggle<\/a><\/noindex> n\u00eb cloudin falas t\u00eb <noindex><a rel=\"nofollow\" href=\"https:\/\/www.dataschool.io\/cloud-services-for-jupyter-notebook\/\">Jupyter Notebook<\/a><\/noindex>.<\/li>\n<li>Prodhuesit e m\u00ebdhenj t\u00eb rrjeteve nervore filluan t\u00eb krijojn\u00eb <strong>\u00abzoo modele\u00bb<\/strong> (model zoo). Me an\u00eb t\u00eb tyre mund t\u00eb krijoni shpejt nj\u00eb aplikacion komercial: <noindex><a rel=\"nofollow\" href=\"https:\/\/tfhub.dev\/\">TF Hub<\/a><\/noindex> p\u00ebr TensorFlow, <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/open-mmlab\/mmdetection\">MMDetection<\/a><\/noindex> p\u00ebr PyTorch, <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/facebookresearch\/Detectron\">Detectron<\/a><\/noindex> p\u00ebr Caffe2, <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/wkentaro\/chainer-modelzoo\">chainer-modelzoo<\/a><\/noindex> p\u00ebr Chainer dhe <noindex><a rel=\"nofollow\" href=\"https:\/\/modelzoo.co\/\">t\u00eb tjera<\/a><\/noindex>.<\/li>\n<li>Rrjetet nervore q\u00eb punojn\u00eb n\u00eb <strong>koh\u00eb reale<\/strong> (real-time) n\u00eb pajisjet mobile. Nga 10 deri n\u00eb 50 kadra n\u00eb sekond\u00eb.<\/li>\n<li>Aplikimi i rrjeteve nervore n\u00eb telefonat (TF Lite), n\u00eb shfletues (TF.js) dhe n\u00eb <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/19ZNz2N79u4\">objekte t\u00eb p\u00ebrdorimit t\u00eb p\u00ebrditsh\u00ebm<\/a><\/noindex> (IoT, <strong>I<\/strong>gjerat <strong>o<\/strong>f <strong>T<\/strong>). Sidomos n\u00eb telefonat q\u00eb tashm\u00eb mb\u00ebshtesin rrjetet nervore n\u00eb nivelin e \u00abharduerit\u00bb (neuroaccelerators).<\/li>\n<li>\u00ab\u00c7do pajisje, objekte veshjeje dhe, ndoshta, madje edhe ushqimi do t\u00eb ken\u00eb <strong>adres\u00ebn IP-v6<\/strong> dhe do t\u00eb komunikojn\u00eb me nj\u00ebra-tjetr\u00ebn\u00bb \u2013 <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/GG7H8Xa4m8I?t=85\">Sebastian Thrun<\/a><\/noindex>.<\/li>\n<li>Rritja e numrit t\u00eb publikimeve p\u00ebr m\u00ebsimin motorik ka filluar <noindex><a rel=\"nofollow\" href=\"http:\/\/data-mining.philippe-fournier-viger.com\/too-many-machine-learning-papers\">t\u00eb tejkaloj\u00eb ligjin e Moore<\/a><\/noindex> (dyfishimi \u00e7do dy vjet) q\u00eb nga viti 2015. \u00cbsht\u00eb e qart\u00eb se nevojiten rrjete nervore p\u00ebr analiz\u00ebn e artikujve.<\/li>\n<li>Teknologjit\u00eb e m\u00ebposhtme po fitojn\u00eb popullaritet:\n<ul>\n<li><strong>PyTorch<\/strong> \u2013 popullariteti po rritet me shpejt\u00ebsi dhe, duket se, po kalon TensorFlow.<\/li>\n<li>Zgjedhja automatike e hiperparametrave <strong>AutoML<\/strong> \u2013 popullariteti po rritet ngadal\u00eb.<\/li>\n<li>R\u00ebnia graduale e sakt\u00ebsis\u00eb dhe rritja e shpejt\u00ebsis\u00eb s\u00eb llogaritjeve: <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/rln_kZbYaWc\">logjik\u00eb e paqart\u00eb<\/a><\/noindex>, algoritme <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/MIPkK5ZAsms\">boosting<\/a><\/noindex>, llogaritje t\u00eb pasakta (afrohet), kvantizimi (kur peshat e rrjetit nervor shnd\u00ebrrohen n\u00eb numra t\u00eb plot\u00eb dhe kvantizohen), neuroakselerator\u00ebt.<\/li>\n<li>P\u00ebrkthim <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/e-WB4lfg30M\">nga imazhi n\u00eb tekst<\/a><\/noindex> dhe <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/rAbhypxs1qQ\">nga teksti n\u00eb imazh<\/a><\/noindex>.<\/li>\n<li>Krijimi <noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/OrHLacCDZVQ\">Objekte tridimensionale nga video<\/a><\/noindex>, tani tashm\u00eb n\u00eb koh\u00eb reale.<\/li>\n<li>Pika kryesore n\u00eb DL \u00ebsht\u00eb se ka shum\u00eb t\u00eb dh\u00ebna, por mbledhja dhe etiketimi i tyre nuk \u00ebsht\u00eb e leht\u00eb. Prandaj zhvillohet automatizimi i etiketimit (<noindex><a rel=\"nofollow\" href=\"https:\/\/youtu.be\/NcKTn4C91Yc\">automated annotation<\/a><\/noindex>) p\u00ebr rrjetet nervore me ndihm\u00ebn e rrjeteve nervore.<\/li>\n<\/ul>\n<\/li>\n<li>Me rrjetet nervore, Shkenca Kompjuterike papritur u b\u00eb <strong>shkenc\u00eb eksperimentale<\/strong> dhe ndodhi <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/480348\">kriza e riprodhueshm\u00ebris\u00eb<\/a><\/noindex>.<\/li>\n<li>Parat\u00eb n\u00eb IT dhe popullariteti i rrjeteve nervore u shfaq\u00ebn nj\u00ebkoh\u00ebsisht, kur llogaritjet u b\u00ebn\u00eb nj\u00eb vler\u00eb tregu. Ekonomia nga valuta e arit b\u00ebhet <strong>arit-valut\u00eb-llogarit\u00ebse.<\/strong>Shihni artikullin tim mbi <noindex><a rel=\"nofollow\" href=\"https:\/\/ru.wikipedia.org\/wiki\/%D0%AD%D0%BA%D0%BE%D0%BD%D0%BE%D1%84%D0%B8%D0%B7%D0%B8%D0%BA%D0%B0\">ekonofizik\u00ebn<\/a><\/noindex> dhe arsyen e shfaqjes s\u00eb parave IT.<\/li>\n<\/ul>\n<p><\/p>\n<p>Gradualisht po shfaqet nj\u00eb <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/481844\">metodologji e re e programimit ML\/DL<\/a><\/noindex> (M\u00ebsimi i Makinerive &amp; M\u00ebsimi i Thell\u00eb), e cila bazohet n\u00eb p\u00ebrfaq\u00ebsimin e programit si nj\u00eb bashk\u00ebsi modelesh t\u00eb trajnuara t\u00eb rrjetit nervor.<\/p>\n<p><\/p>\n<p><img decoding=\"async\" alt=\"Rrjet\u00eb nervore. Ku po shkon gjith\u00e7ka\" src=\"\/wp-content\/uploads\/2020\/01\/17df76a613191522145abeba177c2685.png\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><\/p>\n<p>Figura 3 \u2013 ML\/DL si nj\u00eb metodologji e re programimi<\/p>\n<p><\/p>\n<p>Megjithat\u00eb, nuk ka ardhur akoma <strong>\u00abteorit\u00eb e rrjeteve nervore\u00bb<\/strong>, n\u00eb kuad\u00ebr t\u00eb cilit mund t\u00eb mendoni dhe punoni sistematikisht. Ajo q\u00eb tani quhet \u00abteori\u00bb n\u00eb t\u00eb v\u00ebrtet\u00eb jan\u00eb algoritme eksperimentale dhe heuristike.<\/p>\n<p><\/p>\n<p>Lidhje p\u00ebr burimet e mia dhe jo vet\u00ebm:<\/p>\n<p><\/p>\n<ul>\n<li>Njoftime p\u00ebr lajmet n\u00eb Data Science. Kryesisht p\u00ebr p\u00ebrpunimin e imazheve. Kush d\u00ebshiron t\u2019i marr\u00eb, le t\u00eb d\u00ebrgoj\u00eb e-mail (foobar167&lt;gaff-gaff&gt;gmail&lt;dot&gt;com). Lidhjet p\u00ebr artikuj dhe video i d\u00ebrgoj sipas grumbullimit t\u00eb materialeve.<\/li>\n<li>Lista e p\u00ebrgjithshme <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/foobar167\/articles\/blob\/master\/Machine_Learning\/courses_on_machine_learning.md\">e kurseve dhe artikujve<\/a><\/noindex>, t\u00eb cilat kam ndjekur dhe do t\u00eb donja t\u00eb ndiqja.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/foobar167\/articles\/blob\/master\/Ubuntu\/13_Keras_and_TensorFlow_how-tos.md#exercises\">Kurs dhe video p\u00ebr t\u00eb fillestarit<\/a><\/noindex>, me t\u00eb cilat ia vlen t\u00eb filloni t\u00eb studioni rrjetet nervore. Plus broshur\u00ebn <noindex><a rel=\"nofollow\" href=\"https:\/\/foobar167.github.io\/page\/vvedeniye-v-mashinnoye-obucheniye-i-iskusstvennyye-neyronnyye-seti.html\">\u00abHyrje n\u00eb m\u00ebsimin e automatizuar dhe rrjetet nervore artificiale\u00bb<\/a><\/noindex>.<\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/foobar167\/articles\/blob\/master\/Ubuntu\/13_Keras_and_TensorFlow_how-tos.md#tools\">Vegla t\u00eb dobishme<\/a><\/noindex>, ku secili do t\u00eb gjej\u00eb di\u00e7ka interesante p\u00ebr vete.<\/li>\n<li>Jan\u00eb treguar ekstremisht t\u00eb dobishme <strong>kanalet video q\u00eb analizojn\u00eb artikuj shkencor\u00eb<\/strong> n\u00eb Data Science. Gjeni, abonohuni tek ata dhe d\u00ebrgoni lidhje koleg\u00ebve t\u00eb tu dhe mua gjithashtu. Shembuj:\n<ul>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/user\/keeroyz\">Two Minute Papers<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/channel\/UCHB9VepY6kYvZjj0Bgxnpbw\">Henry AI Labs<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/channel\/UCZHmQk67mSJgfCCTn7xBfew\">Yannic Kilcher<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/channel\/UC5_6ZD6s8klmMu9TXEB_1IA\">CodeEmporium<\/a><\/noindex><\/li>\n<li><noindex><a rel=\"nofollow\" href=\"https:\/\/www.dlology.com\">Blogu Chengwei Zhang<\/a><\/noindex> aka <noindex><a rel=\"nofollow\" href=\"https:\/\/github.com\/Tony607\">Tony607<\/a><\/noindex> me udh\u00ebzime hap pas hapi dhe kod t\u00eb hapur.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><\/p>\n<p>Faleminderit p\u00ebr v\u00ebmendjen!<\/p>\n<p>Burimi: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/482794\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0421\u0442\u0430\u0442\u044c\u044f \u0441\u043e\u0441\u0442\u043e\u0438\u0442 \u0438\u0437 \u0434\u0432\u0443\u0445 \u0447\u0430\u0441\u0442\u0435\u0439: \u041a\u0440\u0430\u0442\u043a\u043e\u0435 \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440 \u0441\u0435\u0442\u0435\u0439 \u043f\u043e \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u044e \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432 \u043d\u0430 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0438 \u0438 \u0441\u0435\u0433\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u0438 \u0438\u0437\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0438\u0439 \u0441 \u0441\u0430\u043c\u044b\u043c\u0438 \u043f\u043e\u043d\u044f\u0442\u043d\u044b\u043c\u0438 \u0434\u043b\u044f \u043c\u0435\u043d\u044f \u0441\u0441\u044b\u043b\u043a\u0430\u043c\u0438 \u043d\u0430 \u0440\u0435\u0441\u0443\u0440\u0441\u044b. \u0421\u0442\u0430\u0440\u0430\u043b\u0441\u044f \u0432\u044b\u0431\u0438\u0440\u0430\u0442\u044c \u0432\u0438\u0434\u0435\u043e \u043f\u043e\u044f\u0441\u043d\u0435\u043d\u0438\u044f \u0438 \u0436\u0435\u043b\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u043d\u0430 \u0440\u0443\u0441\u0441\u043a\u043e\u043c \u044f\u0437\u044b\u043a\u0435. \u0412\u0442\u043e\u0440\u0430\u044f \u0447\u0430\u0441\u0442\u044c \u0441\u043e\u0441\u0442\u043e\u0438\u0442 \u0432 \u043f\u043e\u043f\u044b\u0442\u043a\u0435 \u043e\u0441\u043e\u0437\u043d\u0430\u0442\u044c \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435 \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u044f \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439. \u0418 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0439 \u043d\u0430 \u0438\u0445 \u043e\u0441\u043d\u043e\u0432\u0435. \u0420\u0438\u0441\u0443\u043d\u043e\u043a 1 \u2013 \u041f\u043e\u043d\u0438\u043c\u0430\u0442\u044c [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-54780","post","type-post","status-publish","format-standard","hentry","category-novosti-interneta"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0421\u0442\u0430\u0442\u044c\u044f \u0441\u043e\u0441\u0442\u043e\u0438\u0442 \u0438\u0437 \u0434\u0432\u0443\u0445 \u0447\u0430\u0441\u0442\u0435\u0439: \u041a\u0440\u0430\u0442\u043a\u043e\u0435 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\u0420\u0438\u0441\u0443\u043d\u043e\u043a 1 \u2013 \u041f\u043e\u043d\u0438\u043c\u0430\u0442\u044c\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/nejroseti-kuda-eto-vse-dvizhetsya\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2020-01-03T21:00:00+00:00\" \/>\n\t\t<meta property=\"article:modified_time\" content=\"2020-02-18T11:02:50+00:00\" \/>\n\t\t<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<!-- All in One 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Ku po shkon gjith\u00e7ka | ProHoster","description":"Artikulli p\u00ebrb\u00ebhet nga dy pjes\u00eb: Nj\u00eb p\u00ebrmbledhje e disa arkitekturave t\u00eb rrjeteve p\u00ebr zbulimin e objekteve n\u00eb imazhe dhe segmentimin e imazheve me lidhjet m\u00eb t\u00eb qarta p\u00ebr mua n\u00eb burime. Kam p\u00ebrpiqur t\u00eb zgjedh video shpjeguese dhe preferueshme n\u00eb gjuh\u00ebn shqipe. Pjesa e dyt\u00eb p\u00ebrb\u00ebn nj\u00eb p\u00ebrpjekje p\u00ebr t\u00eb kuptuar drejtimin e zhvillimit t\u00eb arkitekturave t\u00eb rrjeteve nervore. Dhe teknologjive t\u00eb bazuara mbi to. Figura 1 \u2013 T\u00eb kuptuarit.","canonical_url":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/nejroseti-kuda-eto-vse-dvizhetsya","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"sq_AL","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47\u041d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438. \u041a\u0443\u0434\u0430 \u044d\u0442\u043e \u0432\u0441\u0435 \u0434\u0432\u0438\u0436\u0435\u0442\u0441\u044f | ProHoster","og:description":"\u0421\u0442\u0430\u0442\u044c\u044f \u0441\u043e\u0441\u0442\u043e\u0438\u0442 \u0438\u0437 \u0434\u0432\u0443\u0445 \u0447\u0430\u0441\u0442\u0435\u0439: 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