{"id":30936,"date":"2019-10-31T21:38:20","date_gmt":"2019-10-31T18:38:20","guid":{"rendered":"https:\/\/prohoster.info\/blog\/mashinnoe-obuchenie-bez-python-anaconda-i-prochih-presmykayushhihsya\/"},"modified":"2019-10-31T21:38:20","modified_gmt":"2019-10-31T18:38:20","slug":"mashinnoe-obuchenie-bez-python-anaconda-i-prochih-presmykayushhihsya","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sq\/blog\/novosti-interneta\/mashinnoe-obuchenie-bez-python-anaconda-i-prochih-presmykayushhihsya","title":{"rendered":"M\u00ebsimi i makin\u00ebs pa Python, Anaconda dhe reptil t\u00eb tjer\u00eb","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Jo, sigurisht, nuk e them krejt seriozisht. Duhet t\u00eb ket\u00eb nj\u00eb limit deri n\u00eb \u00e7far\u00eb shkalle \u00ebsht\u00eb e mundur t\u00eb thjeshtohet nj\u00eb tem\u00eb. Por p\u00ebr hapat e par\u00eb, p\u00ebr t\u00eb kuptuar konceptet bazike dhe p\u00ebr t'u p\u00ebrshtatur shpejt n\u00eb tem\u00eb, ndoshta \u00ebsht\u00eb e pranueshme. Si t\u00eb quajm\u00eb k\u00ebt\u00eb material sakt\u00ebsisht (opsionet: \"M\u00ebsimi i makinerive p\u00ebr fillestar\u00eb\", \"Analiza e t\u00eb dh\u00ebnave q\u00eb nga e para\", \"Algoritmet p\u00ebr m\u00eb t\u00eb vegjlit\"), do ta diskutojm\u00eb n\u00eb fund. <\/p>\n

T\u00eb kalojm\u00eb n\u00eb tem\u00eb. Kam shkruar disa programe praktike n\u00eb MS Excel p\u00ebr vizualizimin dhe paraqitjen e qart\u00eb t\u00eb proceseve q\u00eb ndodhin n\u00eb metoda t\u00eb ndryshme t\u00eb m\u00ebsimit t\u00eb makinerive gjat\u00eb analiz\u00ebs s\u00eb t\u00eb dh\u00ebnave. T\u00eb shoh\u00ebsh \u00ebsht\u00eb t\u00eb besosh, n\u00eb fund t\u00eb fundit, si\u00e7 thon\u00eb ata q\u00eb e zhvilluan shumic\u00ebn e k\u00ebtyre metodave (nd\u00ebr t\u00eb tjera, jo t\u00eb gjitha. Metoda shum\u00eb e fuqishme e \"vektoreve mb\u00ebshtet\u00ebs\", ose SVM, sip\u00ebrfaqja e mb\u00ebshtetjeve \u2013 shpikje e bashk\u00ebatdhetarit ton\u00eb Vladimir Vapnik, Instituti Menaxhues i Mosk\u00ebs. Vitin 1963, p\u00ebr t\u00eb qen\u00eb t\u00eb sakt\u00eb! Tani, ai megjithat\u00eb, m\u00ebson dhe punon n\u00eb SHBA).<\/p>\n

T\u00eb tri skedat p\u00ebr shqyrtim
\n<\/a><\/noindex><\/p>\n

1. Klasifikimi p\u00ebrmes metod\u00ebs K-means<\/h2>\n

\nK\u00ebto lloj problemesh i p\u00ebrkasin \"m\u00ebsimit pa m\u00ebsues\", kur na nevojitet t\u00eb ndajm\u00eb t\u00eb dh\u00ebnat fillestare n\u00eb nj\u00eb num\u00ebr t\u00eb caktuar kategorish t\u00eb njohura paraprakisht, por atje nuk kemi asnj\u00eb num\u00ebr p\u00ebrgjigjesh \"t\u00eb sakta\", ato duhet t'i nxjerrim nga vet\u00eb t\u00eb dh\u00ebnat. Problemi themelor klasik i gjetjes s\u00eb n\u00ebnllojve t\u00eb luleve t\u00eb irisit (Ronald Fisher, viti 1936!), i cili konsiderohet si filizi i par\u00eb i k\u00ebtyre njohurive \u2013 ka pik\u00ebrisht k\u00ebt\u00eb natyr\u00eb.<\/p>\n

Metoda \u00ebsht\u00eb mjaft e thjesht\u00eb. Ne kemi nj\u00eb grup objektesh, t\u00eb paraqitura si vektor\u00eb (grupe N numrash). P\u00ebr irisat, k\u00ebto jan\u00eb grupe 4 numrash q\u00eb karakterizojn\u00eb lulen: gjat\u00ebsia dhe gjer\u00ebsia e pjes\u00ebs jashtme dhe t\u00eb brendshme t\u00eb petaleve, p\u00ebrkat\u00ebsisht (Iriset e Fishers \u2013 Wikipedia<\/a><\/noindex>). Si distanc\u00eb, ose mas\u00eb af\u00ebrsie midis objekteve, p\u00ebrdoret metrika e zakonshme kartiziane.<\/p>\n

M\u00eb pas, n\u00eb m\u00ebnyr\u00eb t\u00eb rastit (ose jo rast\u00ebsisht, shihni m\u00eb posht\u00eb), zgjidhen qendrat e grupeve, dhe llogariten distancat nga secili objekt n\u00eb qendrat e grupeve. \u00c7do objekt n\u00eb k\u00ebt\u00eb hap t\u00eb iteracionit sh\u00ebnohet se i p\u00ebrket qendr\u00ebs m\u00eb t\u00eb af\u00ebrt. M\u00eb pas, qendra e \u00e7do grupi zhvendoset n\u00eb mesataren aritmetike t\u00eb koordinatave t\u00eb an\u00ebtar\u00ebve t\u00eb saj (p\u00ebr analogji me fizik\u00ebn, e quajn\u00eb gjithashtu \"qendra e mas\u00ebs\"), dhe procedura p\u00ebrs\u00ebritet.<\/p>\n

Procesi konvergon mjaft shpejt. N\u00eb imazhet n\u00eb dy dimensione kjo duket k\u00ebshtu:<\/p>\n

1. Shp\u00ebrndarja fillestare rast\u00ebsore e pikave n\u00eb plan dhe numri i grupeve<\/p>\n

\"M\u00ebsimi
\n
\n2. Caktimi i qendrave t\u00eb grupeve dhe lidhja e pikave me grupet e tyre<\/p>\n

\"M\u00ebsimi
\n
\n3. Zhvendosja e koordinatave t\u00eb qendrave t\u00eb grupeve, rip\u00ebrllogaritja e p\u00ebrkat\u00ebsis\u00eb s\u00eb pikave, derisa qendrat t\u00eb stabilizohen. Duket rruga e l\u00ebvizjes s\u00eb qendr\u00ebs s\u00eb grupit n\u00eb pozitat p\u00ebrfundimtare.<\/p>\n

\"M\u00ebsimi
\n
\nN\u00eb \u00e7do moment mund t\u00eb caktoni qendra t\u00eb reja t\u00eb grupeve (pa gjeneruar nj\u00eb shp\u00ebrndarje t\u00eb re pikash!) dhe t\u00eb shihni se procesi i ndarjes nuk \u00ebsht\u00eb gjithmon\u00eb unik. Matematika do t\u00eb thot\u00eb se n\u00eb funksionin q\u00eb optimizohet (shuma e katror\u00ebve t\u00eb distancave nga piketat n\u00eb qendrat e grupeve t\u00eb tyre) ne gjejm\u00eb jo minimume globale, por lokale. T\u00eb zgjidhim k\u00ebt\u00eb problem mund t\u00eb b\u00ebhet duke zgjedhur pa rast\u00ebsi qendrat fillestare t\u00eb grupeve, ose duke provuar qendra t\u00eb mundshme (ndonj\u00ebher\u00eb \u00ebsht\u00eb p\u00ebr t\u00eb p\u00ebrfituar t'i vendosim sakt\u00ebsisht n\u00eb ndonj\u00eb nga piketat, at\u00ebher\u00eb t\u00eb pakt\u00ebn kemi garancin\u00eb q\u00eb nuk do t\u00eb kemi grupe t\u00eb zbraz\u00ebta). N\u00eb \u00e7do rast, nj\u00eb num\u00ebr i mbaruar ka gjithmon\u00eb nj\u00eb skaj t\u00eb posht\u00ebm t\u00eb sakt\u00eb. <\/p>\n

Mund t\u00eb eksperimentoni me k\u00ebt\u00eb sked\u00eb n\u00eb k\u00ebt\u00eb lidhje<\/a><\/noindex> (mos harroni t\u00eb aktivizoni mb\u00ebshtetje p\u00ebr makros). Skedar\u00ebt jan\u00eb verifikuar nga viruset)<\/p>\n

P\u00ebrshkrimi i metod\u00ebs n\u00eb Wikipedia \u2013 Metoda K-means<\/a><\/noindex><\/p>\n

2. Aproksimimi me polinome dhe ndarja e t\u00eb dh\u00ebnave. P\u00ebrshtatja e tepruar<\/h2>\n

\nShkenc\u00ebtari dhe popullarizuesi i shkenc\u00ebs mbi t\u00eb dh\u00ebnat, K.V. Voroncov thot\u00eb shkurtimisht p\u00ebr metodat e m\u00ebsimit t\u00eb makinerive si \"shkenca e kalimit t\u00eb vijave p\u00ebrmes pikave\". N\u00eb k\u00ebt\u00eb shembull, do t\u00eb gjejm\u00eb ligjshm\u00ebrin\u00eb n\u00eb t\u00eb dh\u00ebna p\u00ebrmes metod\u00ebs s\u00eb katror\u00ebve m\u00eb t\u00eb vegj\u00ebl. <\/p>\n

Tregohet teknika e ndarjes s\u00eb t\u00eb dh\u00ebnave fillestare n\u00eb \"t\u00eb m\u00ebsuara\" dhe \"t\u00eb kontrolluara\", si dhe fenomeni si p\u00ebrshtatja e tepruar, ose \"p\u00ebrshtatja e tep\u00ebrt\" n\u00eb t\u00eb dh\u00ebna. Me nj\u00eb aproksimim t\u00eb sakt\u00eb do t\u00eb kemi nj\u00eb gabim t\u00eb caktuar n\u00eb t\u00eb dh\u00ebnat e m\u00ebsuara dhe disa gabime m\u00eb t\u00eb m\u00ebdha \u2013 n\u00eb t\u00eb dh\u00ebnat e kontrollit. N\u00eb rastin e gabuar \u2013 p\u00ebrshtatja e sakt\u00eb n\u00eb t\u00eb dh\u00ebnat e m\u00ebsuara dhe nj\u00eb gabim t\u00eb madh n\u00eb ato t\u00eb kontrollit.<\/p>\n

(Fakti i njohur \u00ebsht\u00eb se p\u00ebrmes N pikave mund t\u00eb kaloni vet\u00ebm nj\u00eb kurb\u00eb t\u00eb grad\u00ebs N-1, dhe ky m\u00ebnyr\u00eb n\u00eb rastin e p\u00ebrgjithsh\u00ebm nuk jep rezultatin e d\u00ebshiruar. Polinomi i interpolimit t\u00eb Lagranzha n\u00eb Wikipedia<\/a><\/noindex>)<\/p>\n

1. Caktojme shp\u00ebrndarjen fillestare<\/p>\n

\"M\u00ebsimi
\n
\n2. Ndajm\u00eb pik\u00ebt n\u00eb \"t\u00eb m\u00ebsuara\" dhe \"t\u00eb kontrolluara\" n\u00eb nj\u00eb raport 70 me 30.<\/p>\n

\"M\u00ebsimi
\n
\n3. Ne lidhim nj\u00eb kurb\u00eb aproksimuese me piketat e trajnimit, shohim gabimin q\u00eb ajo jep n\u00eb t\u00eb dh\u00ebnat e kontrollit.<\/p>\n

\"M\u00ebsimi
\n
\n4. Ne lidhim nj\u00eb kurb\u00eb t\u00eb sakt\u00eb p\u00ebrmes pikave t\u00eb trajnimit dhe shohim nj\u00eb gabim monstruoz n\u00eb t\u00eb dh\u00ebnat e kontrollit (dhe zero n\u00eb ato t\u00eb trajnimit, por \u00e7far\u00eb ndihme ka kjo?).<\/p>\n

\"M\u00ebsimi
\n
\nKjo \u00ebsht\u00eb, natyrisht, varianti m\u00eb i thjesht\u00eb me nj\u00eb ndarje n\u00eb n\u00ebnsetet 'trajnues' dhe 'kontrollues', n\u00eb rastin e p\u00ebrgjithsh\u00ebm kjo b\u00ebhet shum\u00eb her\u00eb p\u00ebr nj\u00eb p\u00ebrputhje m\u00eb t\u00eb mir\u00eb t\u00eb koeficient\u00ebve.<\/p>\n

Skedari \u00ebsht\u00eb i disponuesh\u00ebm k\u00ebtu, \u00ebsht\u00eb kontrolluar nga antivirus.<\/a><\/noindex> Aktivizoni makrot p\u00ebr funksionimin e sakt\u00eb.<\/p>\n

3. R\u00ebnia e gradienteve dhe dinamika e ndryshimit t\u00eb gabimit.<\/h2>\n

\nK\u00ebtu do t\u00eb jet\u00eb rasti 4-dimensional dhe regresioni linear. Koeficient\u00ebt e regresionit linear do t\u00eb p\u00ebrcaktohen hap pas hapi me metod\u00ebn e r\u00ebnies s\u00eb gradienteve, fillimisht t\u00eb gjith\u00eb koeficient\u00ebt jan\u00eb zero. N\u00eb nj\u00eb grafik t\u00eb ve\u00e7ant\u00eb shikohet dinamika e reduktimit t\u00eb gabimit me p\u00ebrshtatjen gjithnj\u00eb e m\u00eb t\u00eb sakt\u00eb t\u00eb koeficient\u00ebve. Ka mund\u00ebsin\u00eb t\u00eb shihen t\u00eb kat\u00ebr projeksionet 2-dimensionale.<\/p>\n

N\u00ebse caktojm\u00eb nj\u00eb hap shum\u00eb t\u00eb madh t\u00eb r\u00ebnies s\u00eb gradienteve, \u00ebsht\u00eb e dukshme se gjithnj\u00eb do t\u00eb kalojm\u00eb minimumin dhe do t\u00eb arrijm\u00eb n\u00eb rezultat p\u00ebr m\u00eb shum\u00eb hapa, megjithat\u00eb, p\u00ebrfundimisht do t\u00eb arrijm\u00eb (p\u00ebrve\u00e7 n\u00ebse nuk e rrisim shum\u00eb hapin e r\u00ebnies \u2014 at\u00ebher\u00eb algoritmi do t\u00eb shkoj\u00eb 'n\u00eb \u00e7menduri'). Dhe grafiku i var\u00ebsis\u00eb s\u00eb gabimit nga hapi i iteracionit do t\u00eb jet\u00eb jo i but\u00eb, por 'i l\u00ebkundur'.<\/p>\n

1. Generojm\u00eb t\u00eb dh\u00ebna, vendosim hapin e r\u00ebnies s\u00eb gradienteve.<\/p>\n

\"M\u00ebsimi
\n
\n2. Me zgjedhjen e duhur t\u00eb hapit t\u00eb r\u00ebnies s\u00eb gradienteve, arrijm\u00eb n\u00eb minimum ngadal\u00eb dhe mjaft shpejt.<\/p>\n

\"M\u00ebsimi
\n
\n3. Me zgjedhjen e gabuar t\u00eb hapit t\u00eb r\u00ebnies s\u00eb gradienteve, kalojm\u00eb maksimumin, grafiku i gabimit \u00ebsht\u00eb 'i l\u00ebkundur', konvergjenca merr m\u00eb shum\u00eb hapa.<\/p>\n

\"M\u00ebsimi
\ndhe<\/p>\n

\"M\u00ebsimi
\n
\n4. Me nj\u00eb zgjedhje krejt\u00ebsisht t\u00eb gabuar t\u00eb hapit t\u00eb r\u00ebnies s\u00eb gradienteve, largohemi nga minimumi.<\/p>\n

\"M\u00ebsimi
\n
\n(P\u00ebr t\u00eb riprodhuar procesin me vlerat e treguara n\u00eb fotografi t\u00eb hapit t\u00eb r\u00ebnies s\u00eb gradienteve, vendosni shenj\u00ebn n\u00eb 't\u00eb dh\u00ebnat referuese').<\/p>\n

Skedari \u2013 n\u00eb k\u00ebt\u00eb link, duhet t\u00eb aktivizoni makrot, nuk ka viruse.<\/a><\/noindex><\/p>\n

Si mendon komuniteti i nderuar, a \u00ebsht\u00eb e pranueshme nj\u00eb thjeshtim i till\u00eb dhe metoda e paraqitjes s\u00eb materialit? A duhet t\u00eb p\u00ebrkthehet artikulli n\u00eb anglisht? <\/b>
\n
Burimi: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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Duhet t\u00eb ket\u00eb nj\u00eb kufi deri n\u00eb \u00e7far\u00eb shkalle \u00ebsht\u00eb e mundur t\u00eb thjeshtohet subjekti. Por p\u00ebr hapat e par\u00eb, kuptimin e koncepteve baz\u00eb dhe 'ngritjen' e shpejt\u00eb n\u00eb tem\u00eb, ndoshta \u00ebsht\u00eb e pranueshme. Dhe si ta em\u00ebrtojm\u00eb sakt\u00ebsisht k\u00ebt\u00eb material (opsionet: 'M\u00ebsimi i makin\u00ebs p\u00ebr fillestar\u00eb', 'Analiza e t\u00eb dh\u00ebnave nga fillimet', 'Algoritmet p\u00ebr m\u00eb t\u00eb vegjlit'), do ta diskutojm\u00eb n\u00eb fund. 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