{"id":30028,"date":"2019-10-31T21:33:18","date_gmt":"2019-10-31T18:33:18","guid":{"rendered":"https:\/\/prohoster.info\/blog\/rabotaem-s-nejrosetyami-chek-list-dlya-otladki\/"},"modified":"2019-10-31T21:33:18","modified_gmt":"2019-10-31T18:33:18","slug":"rabotaem-s-nejrosetyami-chek-list-dlya-otladki","status":"publish","type":"post","link":"https:\/\/prohoster.info\/lv\/blog\/rabotaem-s-nejrosetyami-chek-list-dlya-otladki","title":{"rendered":"Str\u0101d\u0101jam ar neironu t\u012bkliem: p\u0101rbaudes saraksts probl\u0113mu nov\u0113r\u0161anai","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><img decoding=\"async\" alt=\"Str\u0101d\u0101jam ar neironu t\u012bkliem: p\u0101rbaudes saraksts probl\u0113mu nov\u0113r\u0161anai\" src=\"\/wp-content\/uploads\/2019\/03\/8ce45093bfe44092cb25d947c32bb0b5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nProgrammatisko produktu kods ma\u0161\u012bnm\u0101c\u012b\u0161an\u0101s jom\u0101 bie\u017ei ir sare\u017e\u0123\u012bts un diezgan p\u0101rsteidzo\u0161s. K\u013c\u016bdu atkl\u0101\u0161ana un nov\u0113r\u0161ana ir resursietilp\u012bgs uzdevums. Pat visvienk\u0101r\u0161\u0101k\u0101s <noindex><a rel=\"nofollow\" href=\"https:\/\/cs.stanford.edu\/people\/eroberts\/courses\/soco\/projects\/neural-networks\/Architecture\/feedforward.html\">neironu t\u012bklas ar tie\u0161o savienojumu<\/a><\/noindex> prasa nopietnu pieeju t\u012bkla arhitekt\u016brai, svaru inicializ\u0101cijai un t\u012bkla optimiz\u0101cijai. Pat neliela k\u013c\u016bda var novest pie nepat\u012bkam\u0101m probl\u0113m\u0101m.<\/p>\n<p>\u0160is raksts ir velt\u012bts j\u016bsu neironu t\u012bklu atk\u013c\u016bdo\u0161anas algoritmam.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<blockquote><p><b>Skillbox iesaka:<\/b> Praktiskais kurss <noindex><a rel=\"nofollow\" href=\"https:\/\/skillbox.ru\/python\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=PTNDEV&amp;utm_content=articles&amp;utm_term=neuronet\">Python izstr\u0101d\u0101t\u0101js no nulles<\/a><\/noindex>.<\/p>\n<p><b>Atg\u0101din\u0101m:<\/b> <i>visiem \u00abHabr\u00bb las\u012bt\u0101jiem \u2014 atlaide 10 000 rub\u013cu re\u0123istr\u0113joties jebkur\u0101 Skillbox kurs\u0101 ar promo kodu \u00abHabr\u00bb.<\/i><\/p><\/blockquote>\n<p><\/p>\n<h3>Algoritms sast\u0101v no pieciem posmiem:<\/h3>\n<p><\/p>\n<ul>\n<li>vienk\u0101r\u0161s s\u0101kums;<\/li>\n<li>zaud\u0113jumu apstiprin\u0101\u0161ana;<\/li>\n<li>starpposmu rezult\u0101tu un savienojumu p\u0101rbaude;<\/li>\n<li>parametru diagnostika;<\/li>\n<li>darb\u012bbas kontrol\u0113\u0161ana.<\/li>\n<\/ul>\n<p>\nJa jums kaut kas \u0161\u0137iet interesant\u0101ks par cit\u0101m da\u013c\u0101m, varat t\u016bl\u012bt p\u0101riet uz \u0161iem posmiem. <\/p>\n<h3>Vienk\u0101r\u0161s s\u0101kums<\/h3>\n<p>\nNeironu t\u012bklu ar sare\u017e\u0123\u012btu arhitekt\u016bru, regul\u0101ru un m\u0101c\u012bbu \u0101truma pl\u0101not\u0101ju ir gr\u016bt\u0101k atk\u013c\u016bdot nek\u0101 parasto. M\u0113s \u0161eit nedaudz kr\u0101pj\u0101m, jo \u0161is punkts ir netie\u0161i saist\u012bts ar atk\u013c\u016bdo\u0161anu, ta\u010du tas joproj\u0101m ir svar\u012bgs ieteikums.<\/p>\n<p>Vienk\u0101r\u0161a s\u0101kuma b\u016bt\u012bba ir vienk\u0101r\u0161otas mode\u013ca izveide un t\u0101s apm\u0101c\u012bba uz vienu datu kopu (punktu).<\/p>\n<p><b>Vispirms izveidojam vienk\u0101r\u0161otu modeli<\/b><\/p>\n<p>Lai \u0101tri s\u0101ktu, izveidojam nelielu t\u012bklu ar vienu sl\u0113pto sl\u0101ni un p\u0101rbaud\u0101m, vai viss darbojas pareizi. Tad pak\u0101peniski sare\u017e\u0123\u012bjam modeli, p\u0101rbaudot katru jauno strukt\u016bras aspektu (papildu sl\u0101ni, parametru utt.), un virz\u0101mies t\u0101l\u0101k.<\/p>\n<p><b>Apm\u0101c\u0101m modeli uz vienas datu kopas (punkta)<\/b><\/p>\n<p>K\u0101 \u0101tru j\u016bsu projekta darb\u012bbas p\u0101rbaudi varat izmantot m\u0101c\u012bb\u0101m vienu vai divas datu vietas, lai apstiprin\u0101tu, vai sist\u0113ma darbojas pareizi. Neironu t\u012bklam j\u0101par\u0101da 100% precizit\u0101te m\u0101c\u012bb\u0101s un p\u0101rbaud\u0113s. Ja tas t\u0101 nav, tad vai nu modelis ir p\u0101r\u0101k mazs, vai ar\u012b jums jau ir k\u013c\u016bda.<\/p>\n<p>Pat ja viss ir labi, sagatavojiet modeli nepiecie\u0161am\u0101 laika gaid\u012b\u0161anai pirms do\u0161an\u0101s t\u0101l\u0101k.<\/p>\n<h3>Zaud\u0113jumu nov\u0113rt\u0113jums<\/h3>\n<p>\nZaud\u0113jumu nov\u0113rt\u0113\u0161ana ir galvenais veids, k\u0101 preciz\u0113t mode\u013ca veiktsp\u0113ju. Jums j\u0101nodro\u0161ina, ka zaud\u0113jums atbilst uzdevumam, un ka zaud\u0113jumu funkcijas tiek nov\u0113rt\u0113tas pareiz\u0101 skal\u0101. Ja izmantojat vair\u0101k nek\u0101 viena veida zaud\u0113jumus, p\u0101rliecinieties, ka visi tie ir vien\u0101d\u0101 k\u0101rt\u012bb\u0101 un pareizi sakalibruoti.<\/p>\n<p>Ir svar\u012bgi piev\u0113rst uzman\u012bbu s\u0101kotn\u0113jiem zaud\u0113jumiem. P\u0101rbaudiet, cik tuvu faktiskais rezult\u0101ts ir gaid\u0101majam, ja modelis s\u0101k darboties ar nejau\u0161u pie\u0146\u0113mumu. <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#baby\">Andrej Karpaty darb\u0101 pied\u0101v\u0101 sekojo\u0161o<\/a><\/noindex>: \u201eP\u0101rliecinieties, ka sa\u0146emat rezult\u0101tu, kas ir gaid\u0101ms, uzs\u0101kot darbu ar nelielu parametru skaitu. Lab\u0101k ir uzreiz p\u0101rbaud\u012bt datu zaud\u0113jumu (iestatot regul\u0101ri\u0161anas pak\u0101pi uz nulli). Piem\u0113ram, CIFAR-10 ar Softmax klasifikatoru m\u0113s gaid\u0101m, ka s\u0101kotn\u0113jie zaud\u0113jumi b\u016bs 2.302, jo gaid\u0101m\u0101 izklied\u0113t\u0101 varb\u016bt\u012bba ir 0.1 katram klasei (jo ir 10 klases), un Softmax zaud\u0113jums ir negat\u012bv\u0101 logaritmiskais varb\u016bt\u012bbas attiec\u012bbas prec\u012bzajai klasei, kas ir \u2013ln (0.1) = 2.302\u201d.<\/p>\n<p>Bin\u0101ro piem\u0113ru vienk\u0101r\u0161i apr\u0113\u0137ina katram no klas\u0113m. Piem\u0113ram, dati: 20% 0\u2019s un 80% 1\u2019s. Sagaid\u0101mais s\u0101kotn\u0113jais zaud\u0113jums b\u016bs l\u012bdz \u20130,2ln (0,5) \u20130,8ln (0,5) = 0,693147. Ja rezult\u0101ts ir liel\u0101ks par 1, tas var liecin\u0101t, ka neironu t\u012bkla svari nav pareizi l\u012bdzsvaroti vai dati nav norm\u0113ti.<\/p>\n<h3>P\u0101rbaud\u0101m starpposma rezult\u0101tus un savienojumus <\/h3>\n<p>\nNeirot\u012bkla atk\u013c\u016bdo\u0161anai ir svar\u012bgi saprast iek\u0161\u0113jos procesus t\u012bkl\u0101 un atsevi\u0161\u0137u starpposma sl\u0101\u0146u lomu, jo tie ir saist\u012bti. \u0160eit ir tipiskas k\u013c\u016bdas, ar kur\u0101m j\u016bs varat saskarties:<\/p>\n<ul>\n<li>nepareizi izteikumi gradientu atjaunin\u0101\u0161anai;<\/li>\n<li>svara atjaunin\u0101jumi netiek piem\u0113roti;<\/li>\n<li>izn\u012bksto\u0161i vai eksplod\u0113jo\u0161i gradienti (exploding gradients).<\/li>\n<\/ul>\n<p>\nJa gradienta v\u0113rt\u012bbas ir nulles, tas noz\u012bm\u0113, ka m\u0101c\u012b\u0161an\u0101s \u0101trums optimiz\u0113t\u0101j\u0101 ir p\u0101r\u0101k zems, vai ar\u012b j\u016bs esat sastapu\u0161ies ar nepareizu izteikumu gradienta atjaunin\u0101\u0161anai.<\/p>\n<p>Turkl\u0101t vajadz\u0113tu uzraudz\u012bt aktiviz\u0101cijas funkciju, svaru un katra sl\u0101\u0146a atjaunin\u0101jumus. Piem\u0113ram, parametru (svaru un novir\u017eu) atjaunin\u0101jumu apjomam <noindex><a rel=\"nofollow\" href=\"https:\/\/cs231n.github.io\/neural-networks-3\/#summary\">j\u0101b\u016bt 1-e3<\/a><\/noindex>.<\/p>\n<p>Past\u0101v par\u0101d\u012bba, kas ieg\u016bst nosaukumu \u201cDying ReLU\u201d vai <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Vanishing_gradient_problem\">\u201eizn\u012bksto\u0161a gradienta probl\u0113ma\u201d<\/a><\/noindex>, kad ReLU neironi izvada nulli p\u0113c liela negat\u012bva v\u0113rt\u012bbas (bias) atvieglin\u0101\u0161anas svaram. \u0160ie neironi vairs nekad netiks aktiviz\u0113ti nevien\u0101 datu viet\u0101.<\/p>\n<p>J\u016bs varat izmantot gradienta p\u0101rbaudi, lai identific\u0113tu \u0161\u012bs k\u013c\u016bdas, apg\u016bstot gradientu, izmantojot numurisku pieeju. Ja tas ir tuvu apr\u0113\u0137in\u0101tajiem gradientiem, tad atpaka\u013cizplat\u012b\u0161ana ir \u012bstenota pareizi. Lai izveidotu gradienta p\u0101rbaudi, iepaz\u012bstieties ar \u0161iem lieliskajiem resursiem no CS231. <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#gradcheck\">\u0161eit<\/a><\/noindex> un <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/optimization-1\/#gradcompute\">\u0161eit<\/a><\/noindex>, k\u0101 ar\u012b ar <noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/watch?v=P6EtCVrvYPU\">m\u0101c\u012bbu stundu<\/a><\/noindex> no Endrj\u016b Nga (Andrew Nga) \u0161aj\u0101 t\u0113m\u0101.<\/p>\n<p><noindex>Faizans \u0160eiks<\/noindex> nor\u0101da tr\u012bs galven\u0101s metodes neironu t\u012bkla vizualiz\u0113\u0161anai:<\/p>\n<ul>\n<li>Priek\u0161izv\u0113rt\u0113\u0161anas \u2014 vienk\u0101r\u0161as metodes, kas r\u0101da mums apm\u0101c\u012bt\u0101 mode\u013ca kop\u0113jo strukt\u016bru. T\u0101s ietver atsauksmju formas vai filtrus atsevi\u0161\u0137iem neironu t\u012bkla sl\u0101\u0146iem un parametru katr\u0101 sl\u0101n\u012b.<\/li>\n<li> Pamatotas uz aktiviz\u0101cij\u0101m. Taj\u0101s m\u0113s dekod\u0113jam atsevi\u0161\u0137u neironu vai neironu grupu aktiviz\u0101cijas, lai izprastu vi\u0146u funkcijas.<\/li>\n<li> Pamatotas uz gradientiem. \u0160\u012bs metodes m\u0113dz manipul\u0113t ar gradientiem, kas veidojas, ejot uz priek\u0161u un atpaka\u013c m\u0101c\u012bbu mode\u013ca proces\u0101 (iek\u013caujot svar\u012bguma kartes un klases aktiviz\u0101cijas kartes).<\/li>\n<\/ul>\n<p>\nIr vair\u0101ki noder\u012bgi r\u012bki atsevi\u0161\u0137u sl\u0101\u0146u aktiviz\u0101ciju un savienojumu vizualiz\u0113\u0161anai, piem\u0113ram, <noindex><a rel=\"nofollow\" href=\"https:\/\/conx.readthedocs.io\/en\/latest\/Getting%20Started%20with%20conx.html#What-is-ConX?\">ConX<\/a><\/noindex> un <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/tensorboard_histograms\">Tensorboard<\/a><\/noindex>.<\/p>\n<p><img decoding=\"async\" alt=\"Str\u0101d\u0101jam ar neironu t\u012bkliem: p\u0101rbaudes saraksts probl\u0113mu nov\u0113r\u0161anai\" src=\"\/wp-content\/uploads\/2019\/03\/56e7e88983d6772ef482bc8396dd52a2.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <\/p>\n<h3>Parametru diagnostika<\/h3>\n<p>\nNeironu t\u012bklos ir daudz parametru, kas mijiedarbojas sav\u0101 starp\u0101, kas apgr\u016btina optimiz\u0101ciju. Patiess \u0161\u012b sada\u013ca ir akt\u012bvu p\u0113t\u012bjumu priek\u0161mets speci\u0101listiem, t\u0101p\u0113c zem\u0101k min\u0113tie ieteikumi b\u016btu j\u0101uzskata tikai par padomiem, s\u0101kumpunktiem, no kuriem var s\u0101kt.<\/p>\n<p><b>Partijas lielums<\/b> (batch size) \u2014 ir j\u0101b\u016bt pietiekami lielam, lai ieg\u016btu prec\u012bzus gradientu k\u013c\u016bdas nov\u0113rt\u0113jumus, bet pietiekami mazam, lai stohastiskais gradienta nolai\u0161an\u0101s (SGD) var\u0113tu sak\u0101rtot j\u016bsu t\u012bklu. Mazie partiju izm\u0113ri izrais\u012bs \u0101tru konver\u0123enci trok\u0161\u0146a d\u0113\u013c m\u0101c\u012bbu proces\u0101 un v\u0113l\u0101k rad\u012bs gr\u016bt\u012bbas optimiz\u0101cij\u0101. Detaliz\u0113t\u0101k par to ir aprakst\u012bts <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1609.04836\">\u0161eit<\/a><\/noindex>.<\/p>\n<p><b>M\u0101c\u012bbu \u0101trums<\/b> \u2014 p\u0101r\u0101k zems nov brings pie l\u0113nas konver\u0123ences vai risku iestr\u0113gt lok\u0101laj\u0101s minimumos. Taj\u0101 pa\u0161\u0101 laik\u0101 augst\u0101ks m\u0101c\u012bbu \u0101trums rad\u012bs optimiz\u0101cijas sadal\u012bjumu, jo j\u016bs risk\u0113jat \"izlekt\" p\u0101ri dzi\u013cai, bet \u0161aurai funkcion\u0101lajai zudumam. M\u0113\u0123iniet izmantot pl\u0101no\u0161anas \u0101trumu, lai to samazin\u0101tu neironu t\u012bkla m\u0101c\u012bbu proces\u0101. CS231n kurs\u0101. <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/\">ir ir liels sada\u013ca, kas velt\u012bta \u0161ai probl\u0113mai<\/a><\/noindex>.<\/p>\n<p><b>Gradientu apgrie\u0161ana<\/b>\u2014 gradientu apgrie\u0161ana parametru laik\u0101 atpaka\u013cejo\u0161aj\u0101 izplat\u012b\u0161an\u0101 p\u0113c maksim\u0101l\u0101s v\u0113rt\u012bbas vai ierobe\u017eojo\u0161\u0101s normas. Noder, lai risin\u0101tu probl\u0113mas ar jebkuriem lidojo\u0161iem gradientiem, ar kuriem varat saskarties tre\u0161aj\u0101 punkt\u0101.<\/p>\n<p><b>Pakot\u0146u normaliz\u0101cija<\/b> \u2014 tiek izmantota, lai normaliz\u0113tu katra sl\u0101\u0146a ieejas datus, kas \u013cauj atrisin\u0101t iek\u0161\u0113j\u0101 kovari\u0101ta sv\u0101rst\u012b\u0161an\u0101s probl\u0113mu. Ja izmantojat Dropout un Batch Normalization kop\u0101, <noindex><a rel=\"nofollow\" href=\"https:\/\/towardsdatascience.com\/pitfalls-of-batch-norm-in-tensorflow-and-sanity-checks-for-training-networks-e86c207548c8\">iepaz\u012bstieties ar \u0161o rakstu<\/a><\/noindex>.<\/p>\n<p><b>Stohastiskais gradienta kritums (SGD)<\/b> \u2014 ir vair\u0101ki SGD veidi, kas izmanto impulsu, piel\u0101gojamas m\u0101c\u012bbu \u0101trumus un Nesterova metodi. Tom\u0113r nevienam no tiem nav ac\u012bmredzama priek\u0161roc\u012bba attiec\u012bb\u0101 uz m\u0101c\u012b\u0161an\u0101s efektivit\u0101ti vai visp\u0101rin\u0101\u0161anu (<noindex><a rel=\"nofollow\" href=\"http:\/\/ruder.io\/optimizing-gradient-descent\/\">detaliz\u0113ta inform\u0101cija \u0161eit<\/a><\/noindex>).<\/p>\n<p><b>Regul\u0101raiz\u0101cija<\/b> \u2014 ir iz\u0161\u0137iro\u0161a noz\u012bme visp\u0101r\u012bg\u0101s mode\u013ca izveid\u0113, jo t\u0101 pievieno sodu par mode\u013ca sare\u017e\u0123\u012bt\u012bbu vai ekstrem\u0101l\u0101m parametru v\u0113rt\u012bb\u0101m. Tas ir veids, k\u0101 samazin\u0101t mode\u013ca dispersiju, nepalielinot t\u0101 novirzi. Vair\u0101k <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#ratio\">detaliz\u0113tas inform\u0101cijas \u2014 \u0161eit<\/a><\/noindex>.<\/p>\n<p>Lai visu nov\u0113rt\u0113tu pats, j\u0101atsp\u0113jo regul\u0101raiz\u0101cija un j\u0101noskaidro gradientu zaud\u0113jumu datu patst\u0101v\u012bgi.<\/p>\n<p><b>Dro\u0161\u012bba <\/b>\u2014 v\u0113l viena metode j\u016bsu t\u012bkla sak\u0101rto\u0161anai, lai nov\u0113rstu p\u0101rslodzi. M\u0101c\u012bbu laik\u0101 dro\u0161\u012bba tiek veikta tikai ar neirona akt\u012bv\u0101s uztur\u0113\u0161anas varb\u016bt\u012bbu p (hiperparametrs) vai t\u0101 nulles iestat\u012b\u0161anu cit\u0101di. Rezult\u0101t\u0101 t\u012bkls katrai m\u0101c\u012bbu partijai izmanto citu parametru apak\u0161kopu, samazinot konkr\u0113tu parametru izmai\u0146as, kas k\u013c\u016bst domin\u0113jo\u0161i.<\/p>\n<p>Svar\u012bgi: ja izmantojat gan dro\u0161\u012bbu, gan pakot\u0146u normaliz\u0101ciju, esiet uzman\u012bgi ar \u0161o oper\u0101ciju sec\u012bbu vai pat to koplietojumu. Viss \u0161is joproj\u0101m tiek akt\u012bvi apspriests un papildin\u0101ts. \u0160eit ir divas svar\u012bgas diskusijas par \u0161o t\u0113mu <noindex><a rel=\"nofollow\" href=\"https:\/\/stackoverflow.com\/questions\/39691902\/ordering-of-batch-normalization-and-dropout\">uz Stackoverflow<\/a><\/noindex> un <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1801.05134\">Arxiv<\/a><\/noindex>.<\/p>\n<h3>Darba kontrole<\/h3>\n<p>\nRun\u0101 par darba procesu un eksperimentu dokument\u0113\u0161anu. Ja nekas netiek dokument\u0113ts, var tikt aizmirsts, piem\u0113ram, k\u0101da m\u0101c\u012b\u0161an\u0101s \u0101truma vai kla\u0161u svars tiek izmantots. Pateicoties uzraudz\u012bbai, iepriek\u0161\u0113jos eksperimentus var viegli apskat\u012bt un reproduc\u0113t. Tas \u013cauj samazin\u0101t dubl\u0113jo\u0161o eksperimentu skaitu.<\/p>\n<p>Tom\u0113r roku dokument\u0113\u0161ana var k\u013c\u016bt par sare\u017e\u0123\u012btu uzdevumu liel\u0101 darba apjoma d\u0113\u013c. \u0160eit pal\u012bg\u0101 n\u0101k t\u0101di r\u012bki k\u0101 Comet.ml, kas pal\u012bdz autom\u0101tiski re\u0123istr\u0113t datu kopas, koda izmai\u0146as, eksperimentu v\u0113sturi un ra\u017eot mode\u013cus, tostarp svar\u012bgo inform\u0101ciju par j\u016bsu modeli (hiperparametri, mode\u013ca snieguma r\u0101d\u012bt\u0101ji un inform\u0101cija par vidi).<\/p>\n<p>Neironu t\u012bkli var b\u016bt diezgan jut\u012bgi pret neliel\u0101m izmai\u0146\u0101m, kas var novest pie mode\u013ca veiktsp\u0113jas samazin\u0101\u0161an\u0101s. Darba uzraudz\u012bba un dokument\u0113\u0161ana ir pirmais solis, ko vajadz\u0113tu veikt, lai standartiz\u0113tu vidi un model\u0113\u0161anu.<\/p>\n<p><img decoding=\"async\" alt=\"Str\u0101d\u0101jam ar neironu t\u012bkliem: p\u0101rbaudes saraksts probl\u0113mu nov\u0113r\u0161anai\" src=\"\/wp-content\/uploads\/2019\/03\/37b3e4ef97ea39a3d28ffca5c1dbf1e5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nCeru, ka \u0161is ieraksts var k\u013c\u016bt par s\u0101kumpunktu, no kura s\u0101ksiet sava neironu t\u012bkla atk\u013c\u016bdo\u0161anu.<\/p>\n<blockquote><p><b>Skillbox iesaka:<\/b><\/p>\n<ul>\n<li>Divu gadu praktiskais kurss <noindex><a rel=\"nofollow\" href=\"https:\/\/iamwebdev.skillbox.ru\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=WEBDEVPRO&amp;utm_content=articles&amp;utm_term=neuronet\">\u201eEs esmu PRO t\u012bmek\u013ca izstr\u0101d\u0101t\u0101js\u201d<\/a><\/noindex>.<\/li>\n<li>Tie\u0161saistes kurss <noindex><a rel=\"nofollow\" href=\"https:\/\/skillbox.ru\/c-sharp\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=CSHDEV&amp;utm_content=articles&amp;utm_term=neuronet\">\u00abC# izstr\u0101d\u0101t\u0101js no 0\u00bb<\/a><\/noindex>.<\/li>\n<li>Praktiskais gada kurss <noindex><a rel=\"nofollow\" href=\"https:\/\/skillbox.ru\/php\/?utm_source=skillbox.media&amp;utm_medium=habr.com&amp;utm_campaign=PHPDEV&amp;utm_content=articles&amp;utm_term=neuronet\">\u201ePHP izstr\u0101d\u0101t\u0101js no 0 l\u012bdz PRO\u201d<\/a><\/noindex>.\n<\/li>\n<\/ul>\n<\/blockquote>\n<p>Avots: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/skillbox\/blog\/444684\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041a\u043e\u0434 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0447\u0430\u0441\u0442\u043e \u0431\u044b\u0432\u0430\u0435\u0442 \u0441\u043b\u043e\u0436\u043d\u044b\u043c \u0438 \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0437\u0430\u043f\u0443\u0442\u0430\u043d\u043d\u044b\u043c. \u041e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u0438\u0435 \u0438 \u043b\u0438\u043a\u0432\u0438\u0434\u0430\u0446\u0438\u044f \u0431\u0430\u0433\u043e\u0432 \u0432 \u043d\u0435\u043c \u2014 \u0440\u0435\u0441\u0443\u0440\u0441\u043e\u0435\u043c\u043a\u0430\u044f \u0437\u0430\u0434\u0430\u0447\u0430. \u0414\u0430\u0436\u0435 \u043f\u0440\u043e\u0441\u0442\u0435\u0439\u0448\u0438\u0435 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u0441 \u043f\u0440\u044f\u043c\u043e\u0439 \u0441\u0432\u044f\u0437\u044c\u044e \u0442\u0440\u0435\u0431\u0443\u044e\u0442 \u0441\u0435\u0440\u044c\u0435\u0437\u043d\u043e\u0433\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0430 \u043a \u0441\u0435\u0442\u0435\u0432\u043e\u0439 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0435, \u0438\u043d\u0438\u0446\u0438\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u0432\u0435\u0441\u043e\u0432, \u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u0438 \u0441\u0435\u0442\u0438. \u041d\u0435\u0431\u043e\u043b\u044c\u0448\u0430\u044f \u043e\u0448\u0438\u0431\u043a\u0430 \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u0438\u0432\u0435\u0441\u0442\u0438 \u043a \u043f\u043e\u044f\u0432\u043b\u0435\u043d\u0438\u044e \u043d\u0435\u043f\u0440\u0438\u044f\u0442\u043d\u044b\u0445 \u043f\u0440\u043e\u0431\u043b\u0435\u043c. \u042d\u0442\u0430 \u0441\u0442\u0430\u0442\u044c\u044f \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u0430 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0443 \u043e\u0442\u043b\u0430\u0434\u043a\u0438 \u0432\u0430\u0448\u0438\u0445 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439. Skillbox \u0440\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0443\u0435\u0442: [&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":[],"tags":[],"class_list":["post-30028","post","type-post","status-publish","format-standard","hentry"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.5 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041a\u043e\u0434 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u043d\u044b\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" 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