{"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 n\u00e4rviv\u00f5rkudega: t\u00f5rkeotsingu kontrollnimekiri","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><img decoding=\"async\" alt=\"T\u00f6\u00f6tame n\u00e4rviv\u00f5rkudega: t\u00f5rkeotsingu kontrollnimekiri\" src=\"\/wp-content\/uploads\/2019\/03\/8ce45093bfe44092cb25d947c32bb0b5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nMasin\u00f5ppe tarkvara kood on sageli keeruline ja \u00fcsna segane. Veaparandamine on ressursimahukas \u00fclesanne. Isegi lihtsaimad <noindex><a rel=\"nofollow\" href=\"https:\/\/cs.stanford.edu\/people\/eroberts\/courses\/soco\/projects\/neural-networks\/Architecture\/feedforward.html\">otsese \u00fchendusega n\u00e4rviv\u00f5rgud<\/a><\/noindex> n\u00f5uavad t\u00f5sist l\u00e4henemist v\u00f5rguehitusele, kaalude initsialiseerimisele ja v\u00f5rgu optimeerimisele. V\u00e4ike viga v\u00f5ib p\u00f5hjustada ebameeldivaid probleeme.<\/p>\n<p>See artikkel k\u00e4sitleb teie n\u00e4rviv\u00f5rkude t\u00f5rkeotsimisalgoritmi.<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 algusest peale<\/a><\/noindex>.<\/p>\n<p><b>Tuletame meelde:<\/b> <i>k\u00f5igile \u00abHabra\u00bb lugejatele \u2014 10 000 rubla soodustus, kui registreerite end Skillboxi mis tahes kursusele promokoodi \u00abHabr\u00bb abil.<\/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>kahjude kinnitamine;<\/li>\n<li>vaheide ja \u00fchenduste kontrollimine;<\/li>\n<li>parameetrite diagnoosimine;<\/li>\n<li>t\u00f6\u00f6 j\u00e4lgimine.<\/li>\n<\/ul>\n<p>\nKui midagi tundub teile huvitavam kui muu, v\u00f5ite kohe nendele osadele \u00fcle minna. <\/p>\n<h3>Lihtne algus<\/h3>\n<p>\nKompleksse arhitektuuri, regulatsioonide ja \u00f5pikiirusete plaanijaga n\u00e4rviv\u00f5rgu t\u00f5rkeotsimine on keerulisem kui tavalise. Siinkohal oleme natuke kavalad, kuna see punkt on t\u00f5rkeotsimisega kaudselt seotud, kuid see on ikkagi oluline soovitus.<\/p>\n<p>Lihtne algus seisneb lihtsustatud mudeli loomises ja selle \u00f5petamises \u00fches andmekogumis (punktis).<\/p>\n<p><b>Esiteks loome lihtsustatud mudeli<\/b><\/p>\n<p>Kiireks tutvustamiseks loome v\u00e4ikese v\u00f5rgu \u00fche peidetud kihiga ja kontrollime, et k\u00f5ik t\u00f6\u00f6tab korralikult. Seej\u00e4rel t\u00e4iustame mudelit j\u00e4rk-j\u00e4rgult, kontrollides iga uue elemendi (t\u00e4iendava kihi, parameetri jne) struktuuri ning liikume edasi.<\/p>\n<p><b>Treena mudelit \u00fches andmekogumis (punktis)<\/b><\/p>\n<p>Kiireks toimivuse kontrollimiseks v\u00f5ite kasutada \u00fche v\u00f5i kahe andmepunkti treenimiseks, et kinnitada, kas s\u00fcsteem t\u00f6\u00f6tab \u00f5igesti. Tehisn\u00e4rviv\u00f5rk peaks n\u00e4itama 100% t\u00e4psust treeningul ja testimisel. Kui see nii ei ole, siis on mudel liiga v\u00e4ike v\u00f5i teil on juba bugi.<\/p>\n<p>Isegi kui k\u00f5ik on h\u00e4sti, valmistage mudel ette \u00fche v\u00f5i mitme epohhi l\u00e4bimiseks, enne kui edasi liigute.<\/p>\n<h3>Kao hindamine<\/h3>\n<p>\nKao hindamine on peamine viis mudeli toimivuse t\u00e4psustamiseks. Peate veenduma, et kadu vastab \u00fclesandele ja kaohinnangud on \u00f5igel skaalal. Kui kasutate rohkem kui \u00fchte kaotuse t\u00fc\u00fcpi, siis veenduge, et need oleksid k\u00f5ik samas suurusj\u00e4rgus ja \u00f5igesti skaleeritud.<\/p>\n<p>Oluline on olla t\u00e4helepanelik algsete kaotuste suhtes. Kontrollige, kui l\u00e4hedal on tegelik tulemus oodatule, kui mudel alustas juhuslikult. V <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#baby\">Andrei Karpatise t\u00f6\u00f6s on ette n\u00e4htud j\u00e4rgmist<\/a><\/noindex>: \u00abVeenduge, et saate tulemuse, mida eeldatakse, kui alustate v\u00e4ikese arvu parameetritega. On parem kohe kontrollida andmete kadu (seades regulatsioonitaseme nulli). N\u00e4iteks CIFAR-10 jaoks Softmax klassifikaatoriga ootame, et algsed kaotused oleksid 2.302, kuna oodatud difuusne t\u00f5en\u00e4osus on 0,1 iga klassi kohta (kuna klasside arv on 10), ning Softmaxi kadu on korrektse klassi negatiivne logaritmiline t\u00f5en\u00e4osus, nagu \u2013ln (0.1) = 2.302\u00bb.<\/p>\n<p>\u0414\u043b\u044f \u0431\u0438\u043d\u0430\u0440\u043d\u043e\u0433\u043e \u043f\u0440\u0438\u043c\u0435\u0440\u0430 \u043f\u0440\u043e\u0441\u0442\u043e \u0434\u0435\u043b\u0430\u0435\u0442\u0441\u044f \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0447\u043d\u044b\u0439 \u0440\u0430\u0441\u0447\u0435\u0442 \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0438\u0437 \u043a\u043b\u0430\u0441\u0441\u043e\u0432. \u0412\u043e\u0442, \u043a \u043f\u0440\u0438\u043c\u0435\u0440\u0443, \u0434\u0430\u043d\u043d\u044b\u0435: 20% 0&#8217;s \u0438 80% 1&#8217;s. \u041e\u0436\u0438\u0434\u0430\u0435\u043c\u0430\u044f \u043d\u0430\u0447\u0430\u043b\u044c\u043d\u0430\u044f \u043f\u043e\u0442\u0435\u0440\u044f \u0441\u043e\u0441\u0442\u0430\u0432\u0438\u0442 \u0434\u043e \u20130,2ln (0,5) \u20130,8ln (0,5) = 0,693147. \u0415\u0441\u043b\u0438 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0431\u043e\u043b\u044c\u0448\u0435 1, \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u0432\u0435\u0441\u0430 \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u0438 \u043d\u0435 \u0441\u0431\u0430\u043b\u0430\u043d\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u044b \u0434\u043e\u043b\u0436\u043d\u044b\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u0438\u043b\u0438 \u0434\u0430\u043d\u043d\u044b\u0435 \u043d\u0435 \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u044b.<\/p>\n<h3>Kontrollime vahepealseid tulemusi ja \u00fchendusi <\/h3>\n<p>\nNeurov\u00f5rkude t\u00f5rkeotsinguks on oluline m\u00f5ista protsesside d\u00fcnaamikat v\u00f5rgu sees ja \u00fcksikute vahekihtide rolli, kuna need on omavahel seotud. Allpool on t\u00fc\u00fcpilised vead, millega v\u00f5ite kokku puutuda:<\/p>\n<ul>\n<li>vale v\u00e4ljendid gradientide uuendamiseks;<\/li>\n<li>kaaluuuendusi ei rakendata;<\/li>\n<li>kadumas v\u00f5i plahvatavad gradientid (exploding gradients).<\/li>\n<\/ul>\n<p>\nKui gradientide v\u00e4\u00e4rtused on null, t\u00e4hendab see, et \u00f5ppimiskiirus optimeerijas on liiga madal, v\u00f5i et olete kokku puutunud vale v\u00e4ljendiga gradientide uuendamiseks.<\/p>\n<p>Lisaks on oluline j\u00e4lgida aktiveerimisfunktsioonide, kaalude ja iga kihi uuenduste v\u00e4\u00e4rtusi. N\u00e4iteks, parametrim\u00e4\u00e4rade (kaalude ja t\u00f5ukude) uuenduste suurus <noindex><a rel=\"nofollow\" href=\"https:\/\/cs231n.github.io\/neural-networks-3\/#summary\">peaks olema 1-e3.<\/a><\/noindex>.<\/p>\n<p>Eksisteerib n\u00e4htus, mida kutsutakse 'Dying ReLU' v\u00f5i <noindex><a rel=\"nofollow\" href=\"https:\/\/en.wikipedia.org\/wiki\/Vanishing_gradient_problem\">\u00abkaduma kippuva gradientide probleemiks\u00bb<\/a><\/noindex>, kui ReLU neuronid annavad p\u00e4rast suurt negatiivset v\u00e4\u00e4rtust (bias) oma kaalude kohta nulli. Need neuronid ei aktiveeru enam kunagi \u00fcheski andmekohas.<\/p>\n<p>Saate gradientide kontrolli kasutada nende vigade tuvastamiseks, hinnates gradienti numbrilise l\u00e4henemise abil. Kui see on l\u00e4hedane arvutatud gradientidele, siis on tagasikandmine \u00f5igesti teostatud. Gradientide kontrollimise loomiseks vaadake neid suurep\u00e4raseid ressursse CS231-st. <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#gradcheck\">siit<\/a><\/noindex> ja <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/optimization-1\/#gradcompute\">siit<\/a><\/noindex>, ning <noindex><a rel=\"nofollow\" href=\"https:\/\/www.youtube.com\/watch?v=P6EtCVrvYPU\">\u00f5ppetundi<\/a><\/noindex> Andrew Nga kohta selle teema osas.<\/p>\n<p><noindex>Faizan Sheikh<\/noindex> toob esile kolm peamist meetodit n\u00e4rviv\u00f5rgu visualiseerimiseks:<\/p>\n<ul>\n<li>Eelv\u00e4ljad \u2014 lihtsad meetodid, mis n\u00e4itavad meile koolitatud mudeli \u00fcldstruktuuri. Need h\u00f5lmavad individuaalsete kihtide neuronite v\u00f5i filtrite v\u00e4ljundite ja iga kihi parameetrite kuvamist.<\/li>\n<li> Aktiveerimise alusel. Nendes t\u00f5lgime individuaalsete neuronite v\u00f5i neuronigruppide aktiveerimisi, et m\u00f5ista nende funktsioone.<\/li>\n<li> Gradientide alusel. Need meetodid kipuvad manipuleerima gradientidega, mis tekivad mudeli koolitamise k\u00e4igus (sealhulgas t\u00e4henduskaardid ja klassi aktiveerimise kaardid).<\/li>\n<\/ul>\n<p>\nOn mitmeid kasulikke t\u00f6\u00f6riistu, mis visualiseerivad kihtide aktiveerimisi ja \u00fchendusi, 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 n\u00e4rviv\u00f5rkudega: t\u00f5rkeotsingu kontrollnimekiri\" src=\"\/wp-content\/uploads\/2019\/03\/56e7e88983d6772ef482bc8396dd52a2.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <\/p>\n<h3>Parameetrite diagnoosimine<\/h3>\n<p>\nNeuraalsetel v\u00f5rkudel on palju parameetreid, mis omavahel suhtlevad, mis muudab optimeerimise keeruliseks. See jaotis on aktiivsete teadusuuringute objektiks, seega tuleks allpool esitatud soovitusi k\u00e4sitleda vaid n\u00f5uannete ja alguspunktidena, millest alustada.<\/p>\n<p><b>Paketi suurus<\/b> (partii suurus) \u2014 peaks olema piisavalt suur, et saada t\u00e4psed gradientvea hinnangud, kuid piisavalt v\u00e4ike, et stohhastiline gradientne langetamine (SGD) suudaks teie v\u00f5rku j\u00e4rjekorda seada. V\u00e4ikesed partii suurused toovad kaasa kiire konvergentsi treeningprotsessi m\u00fcra t\u00f5ttu ja hiljem \u2014 optimeerimise raskusi. T\u00e4iendavat teavet leiate siit <noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1609.04836\">siit<\/a><\/noindex>.<\/p>\n<p><b>\u00d5ppekiirus<\/b> \u2014 liiga madal viib aeglasele konvergentsile v\u00f5i riskile j\u00e4\u00e4da kinni kohalikesse miinimumitesse. Samal ajal p\u00f5hjustab k\u00f5rge \u00f5ppekiirus optimeerimise hajumist, kuna te riskite \u201eh\u00fcppama\u201d s\u00fcgavale, kuid kitsale kaotuse funktsiooni osale. Proovige kasutada kiirusplaneerimist, et \u00f5ppimise ajal seda v\u00e4hendada. CS231n kursusel <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/\">on suur osa, mis k\u00e4sitleb seda teemat<\/a><\/noindex>.<\/p>\n<p><b>Gradientide piiramine<\/b>\u200a \u2014 gradientide piiramine tagasikandmise ajal maksimaalse v\u00e4\u00e4rtuse v\u00f5i piirm\u00e4\u00e4ra j\u00e4rgi. See on kasulik, kui satute kokku plahvatuvate gradientidega, nagu mainitud kolmandas punktis.<\/p>\n<p><b>Partii normaliseerimine<\/b> \u2014 kasutatakse iga kihi sisendi normaliseerimiseks, mis aitab lahendada sisemist kovariatsioonimuutust. Kui kasutate koos Dropout'i ja Batch Normalization'i, <noindex><a rel=\"nofollow\" href=\"https:\/\/towardsdatascience.com\/pitfalls-of-batch-norm-in-tensorflow-and-sanity-checks-for-training-networks-e86c207548c8\">vaadake seda artiklit<\/a><\/noindex>.<\/p>\n<p><b>Stohhastiline gradientide langetamine (SGD)<\/b> \u2014 on mitmeid SGD variante, mis kasutavad impulssi, kohandatud \u00f5ppimiskiirus ja Nesterovi meetodit. Kuid \u00fckski neist ei oma selget eeliseid ei \u00f5ppimise efektiivsuse ega \u00fcldistamise osas (<noindex><a rel=\"nofollow\" href=\"http:\/\/ruder.io\/optimizing-gradient-descent\/\">rohkem teavet siin<\/a><\/noindex>).<\/p>\n<p><b>Regulaarimine<\/b> \u2014 on h\u00e4davajalik \u00fcldistava mudeli ehitamiseks, kuna see lisab karistuse mudeli keerukuse v\u00f5i \u00e4\u00e4rmuslike parameetrite v\u00e4\u00e4rtuste eest. See on viis v\u00e4hendada mudeli dispersiooni ilma selle kaldena oluliselt suurenemist. \u00dcksikasjalikuma <noindex><a rel=\"nofollow\" href=\"http:\/\/cs231n.github.io\/neural-networks-3\/#ratio\">teabe saamiseks \u2014 vaadake siia<\/a><\/noindex>.<\/p>\n<p>Kuna ise k\u00f5ike hinnata, tuleb regulaarimine v\u00e4lja l\u00fclitada ja kontrollida andmete kadumise gradienti ise.<\/p>\n<p><b>Kukkumine <\/b>\u2014 veel \u00fcks meetod teie v\u00f5rgu organiseerimiseks \u00fclekoormuse v\u00e4ltimiseks. Koolituse ajal toimub kukkumine, s\u00e4ilitades neuroni aktiivsuse mingi t\u00f5en\u00e4osusega p (h\u00fcperparameeter) v\u00f5i seades selle nulliks vastasel juhul. Tulemuseks on, et v\u00f5rk peab kasutama iga \u00f5ppepartii jaoks teistsugust parameetrite alamkogumit, mis v\u00e4hendab teatud parameetrite muutusi, mis v\u00f5ivad domineerivaks muutuda.<\/p>\n<p>Oluline: kui kasutate samaaegselt nii kukkumist kui ka partii normaliseerimist, olge ettevaatlik nende toimingute j\u00e4rjekorra v\u00f5i isegi kooskasutamise osas. K\u00f5ike seda arutatakse ja t\u00e4iendatakse endiselt aktiivselt. 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\u00f6tamise kontroll<\/h3>\n<p>\nTegemist on t\u00f6\u00f6protsesside ja katsete dokumenteerimisega. Kui mitte midagi ei dokumenteerida, v\u00f5ib ununeda n\u00e4iteks, milline on kasutatav \u00f5ppimiskiirus v\u00f5i klasside kaalud. Kontrolli abil on v\u00f5imalik probleemideta vaadata ja korrata varasemaid katseid. See v\u00f5imaldab v\u00e4hendada dubleeritud katsete arvu.<\/p>\n<p>T\u00f5si, k\u00e4sitsi dokumenteerimine v\u00f5ib osutuda keeruliseks, kui t\u00f6\u00f6de maht on suur. Siinkohal tulevad appi sellised t\u00f6\u00f6riistad nagu Comet.ml, mis aitavad automaatselt logida andme kogu, koodimuudatused, katsete ajaloo ja tootemudeleid, sealhulgas olulisi andmeid teie mudeli kohta (h\u00fcperparameetrid, mudeli j\u00f5udluse n\u00e4itajad ja keskkonnaandmed).<\/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, mille tuleks teha keskkonna ja modelleerimise standardiseerimiseks.<\/p>\n<p><img decoding=\"async\" alt=\"T\u00f6\u00f6tame n\u00e4rviv\u00f5rkudega: t\u00f5rkeotsingu kontrollnimekiri\" src=\"\/wp-content\/uploads\/2019\/03\/37b3e4ef97ea39a3d28ffca5c1dbf1e5.png\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\nLoodan, et see postitus suudab olla alguspunkt, millest te alustate oma neuraalv\u00f5rgu h\u00e4\u00e4lestust.<\/p>\n<blockquote><p><b>Skillbox soovitab:<\/b><\/p>\n<ul>\n<li>Kaheaastane praktiline kursus <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\">\u201eMa 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\">\u201eC# arendaja algusest peale\u201c<\/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\">\u201ePHP arendaja algusest PRO-ni\u201c<\/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 4.9.10 - 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 \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. 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