{"id":13887,"date":"2023-08-25T00:00:00","date_gmt":"2023-08-25T00:00:00","guid":{"rendered":"https:\/\/tech-lib.eu\/tech\/jak-funguje-hluboke-uceni\/"},"modified":"2023-08-25T00:00:00","modified_gmt":"2023-08-25T00:00:00","slug":"jak-funguje-hluboke-uceni","status":"publish","type":"post","link":"https:\/\/tech-lib.eu\/tech\/jak-funguje-hluboke-uceni\/","title":{"rendered":"Jak funguje hlubok\u00e9 u\u010den\u00ed"},"content":{"rendered":"
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Co je to um\u011bl\u00e1 inteligence?<\/div>\n
Um\u011bl\u00e1 inteligence – Artificial intelligece (AI) – je schopnost stroj\u016f napodobovat lidsk\u00e9 schopnosti, jako je uva\u017eov\u00e1n\u00ed, u\u010den\u00ed se, pl\u00e1nov\u00e1n\u00ed nebo kreativita. Um\u011bl\u00e1 inteligence umo\u017e\u0148uje technick\u00fdm syst\u00e9m\u016fm reagovat na vn\u011bmy z jejich prost\u0159ed\u00ed, \u0159e\u0161it probl\u00e9my a dosahovat ur\u010dit\u00fdch c\u00edl\u016f.<\/div>\n
\n\t\t\t\t\t Dal\u0161\u00ed informace najdete na<\/span> www.europarl.europa.eu<\/a>\n\t\t\t\t<\/div>\n<\/p><\/div>\n
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V posledn\u00edch letech se t\u00e9ma hlubok\u00e9ho u\u010den\u00ed t\u011b\u0161\u00ed velk\u00e9mu z\u00e1jmu a p\u0159ita\u017elivosti. Jedn\u00e1 se o silnou technologii, kter\u00e1 zm\u011bnila \u0159adu odv\u011btv\u00ed, v\u010detn\u011b financ\u00ed, zdravotnictv\u00ed a z\u00e1bavy. V tomto \u010dl\u00e1nku se pod\u00edv\u00e1me na to, jak hlubok\u00e9 u\u010den\u00ed funguje, jak se pou\u017e\u00edv\u00e1 ve spole\u010dnosti Netflix, jak ho lze jednodu\u0161e vysv\u011btlit a jak obt\u00ed\u017en\u00e9 je jeho pou\u017eit\u00ed. <\/p>\n

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Hlubok\u00e9 u\u010den\u00ed je z\u00e1sadn\u011b ovlivn\u011bno slo\u017een\u00edm a fungov\u00e1n\u00edm lidsk\u00e9ho mozku. Um\u011bl\u00e9 neuronov\u00e9 s\u00edt\u011b jsou v\u00fdpo\u010detn\u00ed modely vytvo\u0159en\u00e9 tak, aby se svou \u010dinnost\u00ed podobaly biologick\u00fdm neuron\u016fm. Tyto um\u011bl\u00e9 neuronov\u00e9 s\u00edt\u011b maj\u00ed n\u011bkolik vrstev, z nich\u017e ka\u017ed\u00e1 m\u00e1 vz\u00e1jemn\u011b propojen\u00e9 uzly, ozna\u010dovan\u00e9 tak\u00e9 jako um\u011bl\u00e9 neurony nebo jednotky. Surov\u00e1 data p\u0159ij\u00edm\u00e1 prvn\u00ed vrstva, zn\u00e1m\u00e1 jako vstupn\u00ed vrstva, a posledn\u00ed vrstva, zn\u00e1m\u00e1 jako v\u00fdstupn\u00ed vrstva, generuje po\u017eadovan\u00fd v\u00fdsledek. <\/p>\n

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U\u010den\u00ed a extrakci p\u0159\u00edznak\u016f ze vstupn\u00edch dat prov\u00e1d\u011bj\u00ed mezivrstvy, zn\u00e1m\u00e9 tak\u00e9 jako skryt\u00e9 vrstvy. Ka\u017ed\u00fd z t\u011bchto uzl\u016f po obdr\u017een\u00ed vstupu z p\u0159edchoz\u00ed vrstvy aplikuje matematickou operaci, \u010dasto v\u00e1\u017een\u00fd sou\u010det, n\u00e1sledovan\u00fd aktiva\u010dn\u00ed funkc\u00ed. S ka\u017edou vrstvou se s\u00ed\u0165 u\u010d\u00ed st\u00e1le slo\u017eit\u011bj\u0161\u00ed a abstraktn\u011bj\u0161\u00ed reprezentaci dat, tento proces se opakuje. Netflix, zn\u00e1m\u00e1 streamovac\u00ed slu\u017eba, vyu\u017e\u00edv\u00e1 hlubok\u00e9 u\u010den\u00ed ke zlep\u0161en\u00ed u\u017eivatelsk\u00e9ho z\u00e1\u017eitku a k nab\u00edzen\u00ed doporu\u010den\u00ed na m\u00edru. K pochopen\u00ed individu\u00e1ln\u00edho vkusu a preferenc\u00ed vyhodnocuj\u00ed algoritmy hlubok\u00e9ho u\u010den\u00ed obrovsk\u00e9 objemy u\u017eivatelsk\u00fdch dat, v\u010detn\u011b historie sledov\u00e1n\u00ed, hodnocen\u00ed a preferenc\u00ed. Netflix dok\u00e1\u017ee p\u0159edv\u00eddat, kter\u00e9 filmy nebo seri\u00e1ly u\u017eivatel pravd\u011bpodobn\u011b ocen\u00ed, a pomoc\u00ed t\u011bchto dat tr\u00e9nuje neuronov\u00e9 s\u00edt\u011b, co\u017e vede ke specializovan\u00fdm n\u00e1vrh\u016fm, kter\u00e9 udr\u017euj\u00ed z\u00e1jem div\u00e1k\u016f. <\/p>\n

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Hlubok\u00e9 u\u010den\u00ed je metoda, kter\u00e1 umo\u017e\u0148uje po\u010d\u00edta\u010d\u016fm u\u010dit se z dat a poskytovat p\u0159esn\u00e9 p\u0159edpov\u011bdi nebo volby. Vynik\u00e1 tam, kde tradi\u010dn\u00ed algoritmy \u010dasto selh\u00e1vaj\u00ed, tedy u slo\u017eit\u00fdch a nestrukturovan\u00fdch dat v\u010detn\u011b fotografi\u00ed, zvuk\u016f a textu. Algoritmy hlubok\u00e9ho u\u010den\u00ed se u\u010d\u00ed samy t\u00edm, \u017ee se u\u010d\u00ed z obrovsk\u00fdch soubor\u016f dat. Aby se sn\u00ed\u017eil rozpor mezi p\u0159edpokl\u00e1dan\u00fdmi v\u00fdstupy a skute\u010dn\u00fdmi hodnotami v tr\u00e9novac\u00edch datech, lad\u00ed neuronov\u00e9 s\u00edt\u011b v pr\u016fb\u011bhu tr\u00e9ninku sv\u00e9 vnit\u0159n\u00ed parametry neboli v\u00e1hy. <\/p>\n

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Slo\u017eitost neuronov\u00fdch s\u00edt\u00ed a obrovsk\u00e9 objemy tr\u00e9novac\u00edch dat zt\u011b\u017euj\u00ed implementaci model\u016f hlubok\u00e9ho u\u010den\u00ed. To obn\u00e1\u0161\u00ed v\u00fdb\u011br spr\u00e1vn\u00fdch n\u00e1vrh\u016f s\u00edt\u00ed, zji\u0161t\u011bn\u00ed, kolik vrstev a uzl\u016f by m\u011bly m\u00edt, a optimalizaci procesu u\u010den\u00ed. Krom\u011b toho, aby bylo mo\u017en\u00e9 efektivn\u011b zpracov\u00e1vat a tr\u00e9novat obrovsk\u00e9 objemy dat, pot\u0159ebuj\u00ed modely hlubok\u00e9ho u\u010den\u00ed \u010dasto velk\u00e9 v\u00fdpo\u010detn\u00ed zdroje, nap\u0159\u00edklad v\u00fdkonn\u00e9 grafick\u00e9 procesory. <\/p>\n

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Z\u00e1v\u011brem lze \u0159\u00edci, \u017ee doporu\u010den\u00ed spole\u010dnosti Netflix na m\u00edru jsou jen jedn\u00edm z p\u0159\u00edklad\u016f toho, jak je hlubok\u00e9 u\u010den\u00ed silnou technologi\u00ed, kter\u00e1 m\u011bn\u00ed mnoho r\u016fzn\u00fdch odv\u011btv\u00ed, v\u010detn\u011b odv\u011btv\u00ed z\u00e1bavy. Algoritmy hlubok\u00e9ho u\u010den\u00ed se dok\u00e1\u017e\u00ed u\u010dit z dat a vytv\u00e1\u0159et p\u0159esn\u00e9 p\u0159edpov\u011bdi, proto\u017ee se velmi podobaj\u00ed struktu\u0159e a fungov\u00e1n\u00ed lidsk\u00e9ho mozku. V\u00fdvoj model\u016f hlubok\u00e9ho u\u010den\u00ed m\u016f\u017ee b\u00fdt obt\u00ed\u017en\u00fd a n\u00e1ro\u010dn\u00fd na zdroje, proto\u017ee vy\u017eaduje znalosti neuronov\u00fdch s\u00edt\u00ed a p\u0159\u00edstup k velk\u00fdm objem\u016fm dat. P\u0159edpokl\u00e1d\u00e1 se, \u017ee hlubok\u00e9 u\u010den\u00ed bude s rozvojem technologi\u00ed st\u00e1le roz\u0161i\u0159ovat mo\u017enosti po\u010d\u00edta\u010d\u016f.<\/p><\/div>\n

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FAQ<\/div>\n
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Je hlubok\u00e9 u\u010den\u00ed snadn\u00e9?<\/div>\n

Hlubok\u00e9 u\u010den\u00ed \u010dasto nen\u00ed pova\u017eov\u00e1no za jednoduch\u00e9, proto\u017ee se jedn\u00e1 o sofistikovan\u00e9 a \u0161pi\u010dkov\u00e9 t\u00e9ma v\u00fdzkumu. Nutn\u00e9 jsou tak\u00e9 siln\u00e9 znalosti programov\u00e1n\u00ed, matematiky, statistiky a pr\u00e1ce s obrovsk\u00fdmi soubory dat. Konstrukce a tr\u00e9nov\u00e1n\u00ed model\u016f hlubok\u00e9ho u\u010den\u00ed m\u016f\u017ee tak\u00e9 zabrat spoustu \u010dasu a v\u00fdpo\u010detn\u00edho v\u00fdkonu. Ka\u017ed\u00fd v\u0161ak m\u016f\u017ee studovat a st\u00e1t se odborn\u00edkem na techniky hlubok\u00e9ho u\u010den\u00ed, pokud vynalo\u017e\u00ed spr\u00e1vn\u00e9 prost\u0159edky a \u00fasil\u00ed. <\/p>\n<\/div>\n

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Pou\u017e\u00edv\u00e1 spole\u010dnost Spotify hlubok\u00e9 u\u010den\u00ed?<\/div>\n

Ano, doporu\u010dovac\u00ed syst\u00e9my spole\u010dnosti Spotify vyu\u017e\u00edvaj\u00ed hlubok\u00e9 u\u010den\u00ed. Aby mohly z\u00e1kazn\u00edk\u016fm poskytovat individualizovan\u00e9 n\u00e1vrhy hudby, algoritmy hlubok\u00e9ho u\u010den\u00ed zkoumaj\u00ed obrovsk\u00e9 mno\u017estv\u00ed dat, v\u010detn\u011b poslechov\u00fdch n\u00e1vyk\u016f u\u017eivatel\u016f, preferenc\u00ed hudebn\u00edch \u017e\u00e1nr\u016f a vlastnost\u00ed skladeb. Spole\u010dnost Spotify m\u016f\u017ee pomoc\u00ed technik hlubok\u00e9ho u\u010den\u00ed vytv\u00e1\u0159et p\u0159esn\u011bj\u0161\u00ed a p\u0159izp\u016fsoben\u011bj\u0161\u00ed doporu\u010den\u00ed, co\u017e celkov\u011b zlep\u0161uje u\u017eivatelsk\u00fd z\u00e1\u017eitek. <\/p>\n<\/div>\n

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Je CNN technikou hlubok\u00e9ho u\u010den\u00ed?<\/div>\n

Konvolu\u010dn\u00ed neuronov\u00e9 s\u00edt\u011b (CNN) jsou pova\u017eov\u00e1ny za podmno\u017einu metod hlubok\u00e9ho u\u010den\u00ed. CNN jsou velmi vhodn\u00e9 pro \u00falohy, jako je rozpozn\u00e1v\u00e1n\u00ed a klasifikace obrazu, proto\u017ee byly vytvo\u0159eny p\u0159edev\u0161\u00edm ke zpracov\u00e1n\u00ed a vyhodnocov\u00e1n\u00ed vizu\u00e1ln\u00edch dat. Skl\u00e1daj\u00ed se z mnoha vrstev propojen\u00fdch neuron\u016f, kter\u00e9 lze nau\u010dit rozpozn\u00e1vat d\u016fle\u017eit\u00e9 prvky ve vstupn\u00edch datech. D\u00edky sv\u00e9 hlubok\u00e9 konstrukci jsou CNN \u00fa\u010dinn\u00fdm n\u00e1strojem pro r\u016fzn\u00e9 probl\u00e9my po\u010d\u00edta\u010dov\u00e9ho vid\u011bn\u00ed, proto\u017ee se mohou automaticky u\u010dit hierarchick\u00e9 reprezentaci vzor\u016f a dat. <\/p>\n<\/div>\n

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Pro\u010d hlubok\u00e9 u\u010den\u00ed funguje tak dob\u0159e?<\/div>\n

D\u016fvodem, pro\u010d hlubok\u00e9 u\u010den\u00ed funguje tak dob\u0159e, je jeho schopnost efektivn\u011b ch\u00e1pat a reprezentovat slo\u017eit\u00e9 vzory a vztahy v datech. Dosahuje toho pomoc\u00ed hlubok\u00fdch neuronov\u00fdch s\u00edt\u00ed, co\u017e jsou neuronov\u00e9 s\u00edt\u011b s n\u011bkolika vrstvami. D\u00edky tomu, \u017ee se ka\u017ed\u00e1 vrstva neuron\u016f u\u010d\u00ed rozpozn\u00e1vat st\u00e1le abstraktn\u011bj\u0161\u00ed vzory, jsou tyto s\u00edt\u011b schopn\u00e9 samostatn\u011b se u\u010dit hierarchick\u00e9 reprezentaci dat. <\/p>\n

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P\u0159i pr\u00e1ci s vysokorozm\u011brn\u00fdmi a nestrukturovan\u00fdmi daty, jako jsou obr\u00e1zky, zvuky a text, jim hloubka t\u011bchto s\u00edt\u00ed umo\u017e\u0148uje zachytit slo\u017eit\u00e9 a jemn\u00e9 korelace v datech. Modely hlubok\u00e9ho u\u010den\u00ed mohou iterativn\u011b zlep\u0161ovat sv\u016fj v\u00fdkon a dob\u0159e zobec\u0148ovat na nov\u00e9 p\u0159\u00edpady d\u00edky vyu\u017eit\u00ed velk\u00e9ho mno\u017estv\u00ed ozna\u010den\u00fdch dat a metod, jako je zp\u011btn\u00e9 \u0161\u00ed\u0159en\u00ed, k aktualizaci vah s\u00edt\u011b. <\/p>\n

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\u00dasp\u011bch hlubok\u00e9ho u\u010den\u00ed se p\u0159ipisuje tak\u00e9 jeho schopnosti automaticky extrahovat reprezentace rys\u016f z nezpracovan\u00fdch dat. Tradi\u010dn\u00ed techniky strojov\u00e9ho u\u010den\u00ed \u010dasto vy\u017eaduj\u00ed ru\u010dn\u00ed tvorbu p\u0159\u00edznak\u016f, p\u0159i n\u00ed\u017e odborn\u00edci na danou problematiku vytv\u00e1\u0159ej\u00ed nejvhodn\u011bj\u0161\u00ed p\u0159\u00edznaky pro model. M\u00edsto toho, aby bylo vy\u017eadov\u00e1no pracn\u00e9 in\u017een\u00fdrstv\u00ed rys\u016f, se hlubok\u00e9 u\u010den\u00ed u\u010d\u00ed tyto rysy ze samotn\u00fdch dat, co\u017e umo\u017e\u0148uje modelu zachytit slo\u017eit\u011bj\u0161\u00ed a jemn\u011bj\u0161\u00ed vzory. <\/p>\n

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V\u00fdvoj obrovsk\u00e9ho objemu dat, paraleln\u00ed zpracov\u00e1n\u00ed a zv\u00fd\u0161en\u00ed kapacity po\u010d\u00edta\u010d\u016f – to v\u0161e bylo pro \u00fasp\u011bch hlubok\u00e9ho u\u010den\u00ed rozhoduj\u00edc\u00ed. Od doby, kdy byly vyvinuty grafick\u00e9 procesory a distribuovan\u00e9 v\u00fdpo\u010detn\u00ed r\u00e1mce, je nyn\u00ed mo\u017en\u00e9 tr\u00e9novat hlubok\u00e9 modely na obrovsk\u00fdch souborech dat, co\u017e vy\u017eaduje velk\u00e9 mno\u017estv\u00ed v\u00fdpo\u010detn\u00edch zdroj\u016f. <\/p>\n

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Celkov\u011b lze pozoruhodn\u00fd \u00fasp\u011bch hlubok\u00e9ho u\u010den\u00ed v r\u016fzn\u00fdch oblastech, jako je po\u010d\u00edta\u010dov\u00e9 vid\u011bn\u00ed, zpracov\u00e1n\u00ed p\u0159irozen\u00e9ho jazyka a rozpozn\u00e1v\u00e1n\u00ed \u0159e\u010di, p\u0159i\u010d\u00edst kombinaci hlubok\u00fdch neuronov\u00fdch s\u00edt\u00ed, schopnosti u\u010dit se reprezentace p\u0159\u00edznak\u016f a dostupnosti v\u00fdpo\u010detn\u00edch zdroj\u016f.<\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Co je to um\u011bl\u00e1 inteligence? Um\u011bl\u00e1 inteligence – Artificial intelligece (AI) – je schopnost stroj\u016f napodobovat lidsk\u00e9 schopnosti, jako je uva\u017eov\u00e1n\u00ed, u\u010den\u00ed se, pl\u00e1nov\u00e1n\u00ed nebo kreativita. Um\u011bl\u00e1 inteligence umo\u017e\u0148uje technick\u00fdm syst\u00e9m\u016fm reagovat na vn\u011bmy z jejich prost\u0159ed\u00ed, \u0159e\u0161it probl\u00e9my a dosahovat ur\u010dit\u00fdch c\u00edl\u016f. Dal\u0161\u00ed informace najdete na www.europarl.europa.eu V posledn\u00edch letech se t\u00e9ma hlubok\u00e9ho u\u010den\u00ed … \u010c\u00edst d\u00e1l<\/a><\/p>\n","protected":false},"author":4622,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5218],"tags":[],"class_list":["post-13887","post","type-post","status-publish","format-standard","hentry","category-hluboke-uceni"],"_links":{"self":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/posts\/13887","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/users\/4622"}],"replies":[{"embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/comments?post=13887"}],"version-history":[{"count":0,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/posts\/13887\/revisions"}],"wp:attachment":[{"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/media?parent=13887"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/categories?post=13887"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tech-lib.eu\/tech\/wp-json\/wp\/v2\/tags?post=13887"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}