{"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\/et\/blog\/rabotaem-s-nejrosetyami-chek-list-dlya-otladki","title":{"rendered":"T\u00f6\u00f6tame tehisintellektiga: t\u00f5rkeotsingu kontrollnimekiri","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><img decoding=\"async\" alt=\"T\u00f6\u00f6tame tehisintellektiga: t\u00f5rkeotsingu kontrollnimekiri\" src=\"\/wp-content\/uploads\/2019\/03\/8ce45093bfe44092cb25d947c32bb0b5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nMasin\u00f5ppe tarkvarakuud on sageli keerulised ja \u00fcsna eksitavad. Vigade avastamine ja k\u00f5rvaldamine on ressursimahukas \u00fclesanne. Isegi k\u00f5ige lihtsamad <noindex><a rel=\"nofollow\" href=\"https:\/\/cs.stanford.edu\/people\/eroberts\/courses\/soco\/projects\/neural-networks\/Architecture\/feedforward.html\">otse\u00fchendusega n\u00e4rviv\u00f5rgud<\/a><\/noindex> n\u00f5uavad t\u00f5sist l\u00e4henemist v\u00f5rgu arhitektuurile, kaalu algatamisele ja v\u00f5rgu optimeerimisele. V\u00e4ike viga v\u00f5ib tuua kaasa ebameeldivaid probleeme.<\/p>\n<p>See artikkel on p\u00fchendatud teie neuronv\u00f5rkude silumise algoritmile.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<blockquote><p><b>Skillbox soovitab:<\/b> Praktiline kursus <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-arendaja alguses<\/a><\/noindex>.<\/p>\n<p><b>Tuletame meelde:<\/b> <i>k\u00f5igile \u00abHabr\u00bb lugejatele \u2013 10 000 rubla allahindlus igale Skillboxi kursusele, kasutades sooduskoodi \u00abHabr\u00bb.<\/i><\/p><\/blockquote>\n<p><\/p>\n<h3>Algoritm koosneb viiest etapist:<\/h3>\n<p><\/p>\n<ul>\n<li>lihtne algus;<\/li>\n<li>kaotuste kinnitamine;<\/li>\n<li>vahepealsete tulemuste ja \u00fchenduste kontrollimine;<\/li>\n<li>parameetrite diagnostika;<\/li>\n<li>t\u00f6\u00f6 j\u00e4lgimine.<\/li>\n<\/ul>\n<p>\nKui midagi tundub teile huvitavam kui \u00fclej\u00e4\u00e4nud, v\u00f5ite kohe nendele jaotistele liikuda. <\/p>\n<h3>Lihtne algus<\/h3>\n<p>\nKeerulise arhitektuuri, regulatsiooni ja \u00f5ppimise kursi plaanijaga neuronv\u00f5rgu silumine on keerulisem kui tavaline. Me natuke petame siin, kuna see punkt seondub silumisega kaudselt, kuid see on siiski oluline soovitus.<\/p>\n<p>Lihtne algus seisneb lihtsustatud mudeli loomises ja selle koolitamises \u00fches andmestikus (punktis).<\/p>\n<p><b>Esmalt loome lihtsustatud mudeli<\/b><\/p>\n<p>Kasutades kiiret algust, loome v\u00e4ikese v\u00f5rgu, millel on ainult \u00fcks peidetud kiht, ja kontrollime, et k\u00f5ik t\u00f6\u00f6taks \u00f5igesti. Siis keerame j\u00e4rk-j\u00e4rgult mudeli keerukamaks, kontrollides iga uut aspekti selle struktuurist (t\u00e4iendavat kihti, parameetrit jne) ja liigume edasi.<\/p>\n<p><b>Koolitame mudelit \u00fches andmestikus (punktis)<\/b><\/p>\n<p>Kiireks kontrollimiseks, kas teie projekt t\u00f6\u00f6tab, v\u00f5ite kasutada \u00fche v\u00f5i kahe andmepunkti koolitamiseks, et kinnitada, kas s\u00fcsteem t\u00f6\u00f6tab \u00f5igesti. Neuronv\u00f5rk peaks n\u00e4itama 100% t\u00e4psust koolitusel ja testimisel. Kui see nii ei ole, on mudel liiga v\u00e4ike v\u00f5i on teil juba viga olemas.<\/p>\n<p>Isegi kui k\u00f5ik on hea, valmistage mudel ette m\u00f6\u00f6dumiseks \u00fche v\u00f5i mitme epohhi kaudu enne, kui liigute edasi.<\/p>\n<h3>Kaotuste hindamine<\/h3>\n<p>\nKaotuste hindamine on peamine meetod mudeli j\u00f5udluse t\u00e4psustamiseks. Teil on oluline veenduda, et kaotus vastab \u00fclesandele ning kaotuse funktsioonid hinnatakse \u00f5ige skaalal. Kui kasutate rohkem kui \u00fchte t\u00fc\u00fcpi kaotust, veenduge, et k\u00f5ik need oleksid \u00fche suurusj\u00e4rgu ja \u00f5igesti skaleeritud.<\/p>\n<p>Oluline on olla t\u00e4helepanelik algsete kaotuste suhtes. Kontrollige, kui l\u00e4hedane on tegelik tulemus oodatavale, kui mudel alustas juhuslikust oletusest. V <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#baby\">Andrei Karpaty t\u00f6\u00f6s on v\u00e4lja pakutud j\u00e4rgnev<\/a><\/noindex>: \u201eVeenduge, et saate tulemuse, mida ootate, alustades v\u00e4ikese arvu parameetritega. On parem kohe kontrollida andmekao (regulaarimise m\u00e4\u00e4ra seadmine nullile). N\u00e4iteks CIFAR-10 puhul, kasutades Softmax klassifikaatorit, oodata algseid kaotusi, mis oleks 2.302, kuna eeldatav difusioonit\u00f5en\u00e4osus on 0,1 iga klassi kohta (kuna klasse on 10), ja Softmax kaotus on korrektse klassi negatiivne logaritmiline t\u00f5en\u00e4osus, st \u2013ln(0.1) = 2.302\u201c.<\/p>\n<p>Binaarse n\u00e4ite puhul tehakse sarnane arvutus iga klassi jaoks. N\u00e4iteks andmed: 20% 0-d ja 80% 1-d. Oodatav algne kaotus on kuni \u20130,2ln (0,5) \u20130,8ln (0,5) = 0,693147. Kui tulemus on suurem kui 1, v\u00f5ib see viidata sellele, et n\u00e4rviv\u00f5rgu kaWeights ei ole korralikult tasakaalustatud v\u00f5i andmed ei ole normaliseeritud.<\/p>\n<h3>Kontrollime vahepealseid tulemusi ja \u00fchendusi <\/h3>\n<p>\nN\u00e4rviv\u00f5rgu t\u00f5rkeotsimiseks on oluline m\u00f5ista protsesside d\u00fcnaamikat v\u00f5rgu sees ja iga vahekihin\u00e4htude rolli, kuna need on omavahel seotud. Siin on t\u00fc\u00fcpilised vead, millega v\u00f5ite kokku puutuda:<\/p>\n<ul>\n<li>vale v\u00e4ljendid gradientide uuendamiseks;<\/li>\n<li>kaalu uuendusi ei rakendata;<\/li>\n<li>kaduvad v\u00f5i plahvatavad gradientid (exploding gradients).<\/li>\n<\/ul>\n<p>\nKui gradientide v\u00e4\u00e4rtused on nullid, t\u00e4hendab see, et \u00f5ppimiskiirus optimeerijas on liiga madal v\u00f5i et olete kokku puutunud vale v\u00e4ljendiga gradientide uuendamiseks.<\/p>\n<p>Samuti tuleb j\u00e4lgida aktivatsioonifunktsioonide, kaalude ja iga kihi uuenduste v\u00e4\u00e4rtusi. N\u00e4iteks parameetrite (kaalude ja nihkete) uuenduste suurus <noindex><a rel=\"nofollow\" href=\"https:\/\/cs231n.github.io\/neural-networks-3\/#summary\">peaks olema 1-e3<\/a><\/noindex>.<\/p>\n<p>On n\u00e4htus, mida nimetatakse \"Dying ReLU\" v\u00f5i <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Vanishing_gradient_problem\">\"kaduvat gradienti\" probleemiks<\/a><\/noindex>, kui ReLU neuronid annavad p\u00e4rast suure negatiivse v\u00e4\u00e4rtuse (bias) \u00f5ppimist nulli. Need neuronid ei aktiveeru enam kunagi \u00fcheski andmekohas.<\/p>\n<p>Saate kasutada gradientide kontrolli, et tuvastada neid vigu, kaudseist gradientide numbrite p\u00f5hjal. Kui see on l\u00e4hedal arvutatud gradientidele, siis tagasikandumine on rakendatud \u00f5igesti. Gradientide kontrolli loomiseks tutvuge nende suurep\u00e4raste ressurssidega CS231-st. <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#gradcheck\">siin<\/a><\/noindex> ja <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/optimization-1\/#gradcompute\">siin<\/a><\/noindex>, samuti <noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/watch?v=P6EtCVrvYPU\">\u00f5petusega<\/a><\/noindex> Andrew Nga kohta sellel teemal.<\/p>\n<p><noindex>Faizan Sheikh<\/noindex> n\u00e4itab kolme peamist meetodit neurov\u00f5rkude visualiseerimiseks:<\/p>\n<ul>\n<li>Eelvaated \u2014 lihtsad meetodid, mis n\u00e4itavad meile koolitatud mudeli \u00fcldstruktuuri. Need h\u00f5lmavad v\u00e4ljaandeid v\u00f5i filtrite vormide n\u00e4itamisest \u00fcksikutes neuronite kihtides ja omadustest igas kihis.<\/li>\n<li> Aktiivsustel p\u00f5hinevad. Neis dekodeerime \u00fcksikute neuronite v\u00f5i neuronigruppide aktiveerimist, et m\u00f5ista nende funktsioone.<\/li>\n<li> Gradientidel p\u00f5hinevad. Need meetodid kipuvad manipuleerima gradientidega, mis tekivad mudeli \u00f5ppimise ajal (sealhulgas olulisuse kaardid ja klassi aktiveerimise kaardid).<\/li>\n<\/ul>\n<p>\nOn mitmeid kasulikke t\u00f6\u00f6riistu aktivatsioonide ja \u00fcksikute kihtide \u00fchenduste visualiseerimiseks, n\u00e4iteks <noindex><a rel=\"nofollow\" href=\"https:\/\/conx.readthedocs.io\/en\/latest\/Getting%20Started%20with%20conx.html#What-is-ConX?\">ConX<\/a><\/noindex> ja <noindex><a rel=\"nofollow\" href=\"https:\/\/www.tensorflow.org\/guide\/tensorboard_histograms\">Tensorboard<\/a><\/noindex>.<\/p>\n<p><img decoding=\"async\" alt=\"T\u00f6\u00f6tame tehisintellektiga: t\u00f5rkeotsingu kontrollnimekiri\" src=\"\/wp-content\/uploads\/2019\/03\/56e7e88983d6772ef482bc8396dd52a2.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <\/p>\n<h3>Parameetrite diagnostika<\/h3>\n<p>\nNeurov\u00f5rkudel on palju parameetreid, mis omavahel suhtlevad, mis muudab optimeerimise keeruliseks. Tegelikult on see jaotis teadlaste aktiivse uurimise teema, seega tuleks allolevaid soovitusi k\u00e4sitleda vaid n\u00e4pun\u00e4idete ja alguspunktidena.<\/p>\n<p><b>Partii suurus<\/b> (batch size) \u2014 see peab olema piisavalt suur, et saada t\u00e4pseid vigade gradientide hinnanguid, kuid piisavalt v\u00e4ike, et stohhastiline gradientide laskmine (SGD) saaks teie v\u00f5rku korraldada. V\u00e4iksemad partii suurused viivad kiirele konvergentsile, t\u00e4nu l\u00e4rmakusele \u00f5ppimisprotsessis ja hiljem \u2014 optimeerimise raskustele. T\u00e4psemalt on see kirjeldatud <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1609.04836\">siin<\/a><\/noindex>.<\/p>\n<p><b>\u00d5ppimiskiirus<\/b> \u2014 liiga madal toob kaasa aeglase konvergentsi v\u00f5i riski kinni j\u00e4\u00e4da kohalikesse miinimumidesse. Samal ajal liiga k\u00f5rge \u00f5ppimiskiirus p\u00f5hjustab optimeerimise hajumist, sest riskite 'h\u00fcppata' s\u00fcgavale, kuid kitsale kadentsifunktsiooni ossale. Proovige kasutada planeeritud kiirus, et seda v\u00e4hendada neurov\u00f5rgu \u00f5ppimise k\u00e4igus. CS231n kursuses <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/\">on suur jaotatud osa, mis on p\u00fchendatud sellele probleemile<\/a><\/noindex>.<\/p>\n<p><b>Gradientide k\u00e4rpimine<\/b>\u200a \u2014 gradientide k\u00e4rpimine tagasiulatuva leviku ajal maksimaalse v\u00e4\u00e4rtuse v\u00f5i piiri normaali j\u00e4rgi. See on kasulik probleemide lahendamiseks, mis on seotud l\u00f5hkevate gradientidega, millega v\u00f5ite kokku puutuda kolmandas punktis.<\/p>\n<p><b>Partii normaliseerimine<\/b> \u2014 kasutatakse iga kihi sisendite normaliseerimiseks, mis aitab lahendada sisemise kovariatiivse nihke probleemi. Kui kasutate Dropouti ja Batch Normat koos, <noindex><a rel=\"nofollow\" href=\"https:\/\/towardsdatascience.com\/pitfalls-of-batch-norm-in-tensorflow-and-sanity-checks-for-training-networks-e86c207548c8\">tutvuge selle artikliga<\/a><\/noindex>.<\/p>\n<p><b>Stohhastiline gradientide langetamine (SGD)<\/b> \u2014 on mitmeid SGD variante, mis kasutavad impulssi, kohandatud \u00f5ppimise kiirus, ja Nesterovi meetod. Samas ei ole \u00fchelgi neist selget eelist ei \u00f5ppimise efektiivsuse ega \u00fcldistamise osas (<noindex><a rel=\"nofollow\" href=\"http:\/\/ruder.io\/optimizing-gradient-descent\/\">t\u00e4iendavad \u00fcksikasjad siin<\/a><\/noindex>).<\/p>\n<p><b>Regulaarimine<\/b> \u2014 on \u00fclioluline \u00fcldistatava mudeli ehitamisel, kuna see lisab trahvi mudeli keerukuse v\u00f5i \u00e4\u00e4rmuslike v\u00e4\u00e4rtuste eest. See on viis mudeli dispersiooni v\u00e4hendamiseks, ilma et see oluliselt suurendaks selle nihket. Rohkem <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#ratio\">t\u00e4iendavat teavet \u2014 siin<\/a><\/noindex>.<\/p>\n<p>Kuna k\u00f5ik ise hinnata, peate regulatsiooni v\u00e4lja l\u00fclitama ja kontrollima kaotuse gradienti ise.<\/p>\n<p><b>Tilkus <\/b>\u2014 on veel \u00fcks meetod teie v\u00f5rgu korraldamiseks \u00fclekoormuse v\u00e4ltimiseks. Koolitamise ajal toimub tilkus ainult neuroni aktiivsuse s\u00e4ilitamise kaudu mingi t\u00f5en\u00e4osusega p (h\u00fcperparameeter) v\u00f5i seadistades selle nulliks vastupidiselt. Seet\u00f5ttu peab v\u00f5rk kasutama iga koolituspartii jaoks erinevat alamkogumit parameetritest, mis v\u00e4hendab teatud parameetrite muutusi, mis muutuvad domineerivaks.<\/p>\n<p>Oluline: kui kasutate nii tilku kui ka partii normaliseerimist, olge ettevaatlik nende toimingute j\u00e4rjekorra v\u00f5i isegi koos kasutamise osas. K\u00f5ik see on endiselt aktiivses arutelus ja t\u00e4iendas. Siin on kaks olulist arutelu selle teema kohta <noindex><a rel=\"nofollow\" href=\"https:\/\/stackoverflow.com\/questions\/39691902\/ordering-of-batch-normalization-and-dropout\">Stackoverflowis<\/a><\/noindex> ja <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1801.05134\">Arxiv<\/a><\/noindex>.<\/p>\n<h3>T\u00f6\u00f6kontroll<\/h3>\n<p>\nR\u00e4\u00e4gime t\u00f6\u00f6protsesside ja katsete dokumenteerimisest. Kui mitte midagi ei dokumenteerita, v\u00f5ib ununeda, milline \u00f5ppimiskiirus v\u00f5i klasside kaalud on kasutusel. T\u00e4nu kontrollile saab mugavalt vaadata ja taastada varasemaid katseid. See aitab v\u00e4hendada korduvate katsete arvu.<\/p>\n<p>Kahjuks v\u00f5ib k\u00e4sitsi dokumenteerimine suure t\u00f6\u00f6mahu korral olla keeruline \u00fclesanne. Siin tulevad appi sellised t\u00f6\u00f6riistad nagu Comet.ml, mis aitavad automaatselt logida andmehulkade, koodimuudatuste, katsete ajalugu ja tootemudeleid, sealhulgas olulisi andmeid teie mudeli kohta (h\u00fcperparameetrid, mudeli j\u00f5udluse n\u00e4itajad ja keskkonna andmed).<\/p>\n<p>Neuraalv\u00f5rk v\u00f5ib olla v\u00e4ga tundlik v\u00e4ikeste muudatuste suhtes, mis v\u00f5ib viia mudeli j\u00f5udluse languseni. T\u00f6\u00f6 j\u00e4lgimine ja dokumenteerimine on esimene samm, mida tuleks astuda keskkonna standardiseerimiseks ja modelleerimiseks.<\/p>\n<p><img decoding=\"async\" alt=\"T\u00f6\u00f6tame tehisintellektiga: t\u00f5rkeotsingu kontrollnimekiri\" src=\"\/wp-content\/uploads\/2019\/03\/37b3e4ef97ea39a3d28ffca5c1dbf1e5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nLoodan, et see postitus saab olema l\u00e4htepunkt, kust alustada oma neuraalv\u00f5rgu h\u00e4\u00e4lestamist.<\/p>\n<blockquote><p><b>Skillbox soovitab:<\/b><\/p>\n<ul>\n<li>Kaks aastat praktilist kursust <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\">\u201eMina olen PRO veebiarendaja\u201c<\/a><\/noindex>.<\/li>\n<li>Veebikursus <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\">C# arendaja 0-st<\/a><\/noindex>.<\/li>\n<li>Praktiline aastakursus <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\">\u00abPHP-arendaja 0-st PRO-ni\u00bb<\/a><\/noindex>.\n<\/li>\n<\/ul>\n<\/blockquote>\n<p>Allikas: <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 5.0.2 - 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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