{"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\/news\/mashinnoe-obuchenie-bez-python-anaconda-i-prochih-presmykayushhihsya","title":{"rendered":"M\u00ebsimi i makinave pa Python, Anaconda dhe reptil\u00ebt e tjer\u00eb","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Nuk, sigurisht, nuk e them k\u00ebt\u00eb seriozisht. Duhet t\u00eb ket\u00eb nj\u00eb kufi se sa larg mund t\u00eb thjeshtohet nj\u00eb tem\u00eb. Por p\u00ebr fazat e para, p\u00ebr t\u00eb kuptuar konceptet baz\u00eb dhe p\u00ebr nj\u00eb kalim t\u00eb shpejt\u00eb n\u00eb tem\u00eb, ndoshta \u00ebsht\u00eb e pranueshme. Si duhet ta em\u00ebrojm\u00eb k\u00ebt\u00eb material (opsionet: \"M\u00ebsimi i makinerive p\u00ebr fillestar\u00ebt\", \"Analiza e t\u00eb dh\u00ebnave n\u00eb hapat e para\", \"Algoritmet p\u00ebr m\u00eb t\u00eb vegjlit\"), do ta diskutojm\u00eb n\u00eb fund. <\/p>\n

Po flasim p\u00ebr pun\u00ebn. Kam shkruar disa programe aplikative n\u00eb MS Excel p\u00ebr vizualizimin dhe paraqitjen e qart\u00eb t\u00eb proceseve q\u00eb ndodhin n\u00eb metodologjit\u00eb e ndryshme t\u00eb m\u00ebsimit t\u00eb makinave gjat\u00eb analiz\u00ebs s\u00eb t\u00eb dh\u00ebnave. Si\u00e7 thon\u00eb anglishtfol\u00ebsit, \"t\u00eb shoh\u00ebsh \u00ebsht\u00eb t\u00eb besosh\", pasi k\u00ebta jan\u00eb ata q\u00eb zhvilluan shumic\u00ebn e k\u00ebtyre metodave (p\u00ebr rastin e SVM, metod\u00ebs m\u00eb t\u00eb fuqishme t\u00eb mb\u00ebshtetjes s\u00eb vektor\u00ebve, q\u00eb \u00ebsht\u00eb shpikur nga bashk\u00ebatdhetari yn\u00eb Vladimir Vapnik, Instituti i Menaxhimit t\u00eb Mosk\u00ebs. Dhe kjo ishte n\u00eb 1963! Tani ai, megjithat\u00eb, m\u00ebson dhe punon n\u00eb SHBA).<\/p>\n

Tri skedar\u00eb p\u00ebr shqyrtim
\n<\/a><\/noindex><\/p>\n

1. Klasifikimi me metod\u00ebn k-means<\/h2>\n

\nK\u00ebto lloj detyrash i p\u00ebrkasin \"m\u00ebsimit pa mbik\u00ebqyrje\", kur na nevojitet t\u00eb ndajm\u00eb t\u00eb dh\u00ebnat e origjin\u00ebs n\u00eb nj\u00eb num\u00ebr t\u00eb caktuar kategorish t\u00eb njohura paraprakisht, por gjithashtu nuk kemi asnj\u00eb sasi \"p\u00ebrgjigjesh t\u00eb sakta\"; ato duhet t\u00eb nxiren nga t\u00eb dh\u00ebnat vet\u00eb. Nj\u00eb detyr\u00eb thelb\u00ebsore klasike p\u00ebr t\u00eb gjetur n\u00ebnllojt\u00eb e luleve t\u00eb iris\u00ebve (Ronald Fisher, 1936!), q\u00eb konsiderohet si fillimi i k\u00ebsaj fushe t\u00eb dijes - \u00ebsht\u00eb pik\u00ebrisht nga ky natyr\u00eb.<\/p>\n

Metoda \u00ebsht\u00eb mjaft e thjesht\u00eb. Ne kemi nj\u00eb grup objektesh, t\u00eb paraqitura si vektora (grupe N numrash). P\u00ebr iris\u00ebt, kjo \u00ebsht\u00eb - grupe 4 numrash, q\u00eb karakterizojn\u00eb lulen: gjat\u00ebsi dhe gjer\u00ebsi e pjes\u00ebve t\u00eb jashtme dhe t\u00eb brendshme t\u00eb petaleve, p\u00ebrkat\u00ebsisht (Iris\u00ebt e Fisherit \u2014 Wikipedia<\/a><\/noindex>). Si distanc\u00eb, ose mas\u00eb af\u00ebrsie midis objekteve, \u00ebsht\u00eb zgjedhur metrika Euclidiane.<\/p>\n

M\u00eb pas, n\u00eb m\u00ebnyr\u00eb t\u00eb rast\u00ebsishme (ose t\u00eb parastisur, shihni m\u00eb posht\u00eb) zgjidhen qendrat e grupeve, dhe llogariten distancat nga \u00e7do objekt deri te qendrat e grupeve. \u00c7do objekt n\u00eb k\u00ebt\u00eb hap t\u00eb iteracionit sh\u00ebnohet si i p\u00ebrkasin qendr\u00ebs m\u00eb t\u00eb af\u00ebrt. M\u00eb pas, qendra e secilit grup zhvendoset n\u00eb mesataren aritmetike t\u00eb koordinatave t\u00eb an\u00ebtar\u00ebve t\u00eb saj (n\u00eb analogji me fizik\u00ebn, quhet gjithashtu \"qendra e mas\u00ebs\"), dhe procedura p\u00ebrs\u00ebritet.<\/p>\n

Procesi p\u00ebrfundon mjaft shpejt. N\u00eb pamjet dy-dimensionale 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. P\u00ebrcaktimi i qendrave t\u00eb grupeve dhe ndarja e pikave n\u00eb grupet e tyre<\/p>\n

\"M\u00ebsimi
\n
\n3. Shkall\u00ebzimi i koordinatave t\u00eb qendrave t\u00eb grupeve, rishikimi i p\u00ebrkat\u00ebsis\u00eb s\u00eb pikave, derisa qendrat t\u00eb stabilizohen. Shikohet 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 p\u00ebrcaktojm\u00eb qendra t\u00eb reja p\u00ebr grupet (pa e gjeneruar nj\u00eb shp\u00ebrndarje t\u00eb re pikash!) dhe t\u00eb shohim se procesi i ndarjes nuk \u00ebsht\u00eb gjithmon\u00eb i qart\u00eb. Matematika e k\u00ebsaj do t\u00eb thot\u00eb se n\u00eb funksionin q\u00eb optimizohet (shuma e katror\u00ebve t\u00eb distancave nga pikat n\u00eb qendrat e grupeve t\u00eb tyre), ne gjejm\u00eb nj\u00eb minimum lokal dhe jo global. T\u00eb zgjidhet ky problem mund t\u00eb arrihet ose me nj\u00eb zgjedhje jo-rast\u00ebsore t\u00eb qendrave t\u00eb grupeve fillestare, ose me kontrollimin e mund\u00ebsive t\u00eb qendrave (ndonj\u00ebher\u00eb ka avantazh q\u00eb t\u2019i vendosim sakt\u00ebsisht n\u00eb nj\u00ebr\u00ebn nga pikat, k\u00ebshtu q\u00eb t\u00eb pakt\u00ebn ka garanci q\u00eb nuk do t\u00eb kemi grupe bosh). N\u00eb \u00e7do rast, nj\u00eb grup finito ka nj\u00eb kufi t\u00eb sakt\u00eb m\u00eb t\u00eb vog\u00ebl. <\/p>\n

Mund t\u00eb eksperimentoni me k\u00ebt\u00eb skedar p\u00ebrmes k\u00ebtij lidhjeje<\/a><\/noindex> (mos harroni t\u00eb aktivizoni mb\u00ebshtetje p\u00ebr makros. Skedar\u00ebt jan\u00eb kontrolluar p\u00ebr viruse)<\/p>\n

P\u00ebrshkrimi i metod\u00ebs n\u00eb Wikipedia \u2014 Metoda k-mesatare<\/a><\/noindex><\/p>\n

2. Aproximi me polinom\u00eb dhe ndarja e t\u00eb dh\u00ebnave. Rishp\u00ebrndarja<\/h2>\n

\nShkenc\u00ebtari dhe popullarizuesi i shkenc\u00ebs s\u00eb t\u00eb dh\u00ebnave K.V. Voroncov thot\u00eb shkurtimisht p\u00ebr metodat e t\u00eb m\u00ebsuarit t\u00eb makinave si \"shkenca e kalimit t\u00eb kurbave p\u00ebrmes pikave\". N\u00eb k\u00ebt\u00eb shembull do t\u00eb gjejm\u00eb nj\u00eb model n\u00eb t\u00eb dh\u00ebna duke p\u00ebrdorur metod\u00ebn e katror\u00ebve minimal\u00eb. <\/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 i till\u00eb si rishp\u00ebrndarja, ose \"ristrukturimi\" sipas t\u00eb dh\u00ebnave. Me nj\u00eb aproximim t\u00eb sakt\u00eb, ne do t\u00eb kemi nj\u00eb gabim n\u00eb t\u00eb dh\u00ebnat e m\u00ebsuara dhe nj\u00eb gabim pak m\u00eb t\u00eb madh - n\u00eb ato t\u00eb kontrolluara. Me nj\u00eb aproximim t\u00eb gabuar - nj\u00eb p\u00ebrshtatje t\u00eb sakt\u00eb p\u00ebr t\u00eb dh\u00ebnat e m\u00ebsuara dhe nj\u00eb gabim t\u00eb madh n\u00eb ato t\u00eb kontrolluara.<\/p>\n

(Fakti i njohur \u00ebsht\u00eb se p\u00ebrmes N pikave mund t\u00eb kalojm\u00eb nj\u00eb kurb\u00eb t\u00eb vetme t\u00eb rendit N-1 dhe ky metod, n\u00eb rast t\u00eb p\u00ebrgjithsh\u00ebm, nuk jep rezultatin e nevojsh\u00ebm. Polinomi interpolues i Lagrange n\u00eb Wikipedia<\/a><\/noindex>)<\/p>\n

1. P\u00ebrcaktojm\u00eb shp\u00ebrndarjen fillestare<\/p>\n

\"M\u00ebsimi
\n
\n2. Ndajm\u00eb pikat n\u00eb \"t\u00eb m\u00ebsuara\" dhe \"t\u00eb kontrolluara\" n\u00eb p\u00ebrqindjen 70 me 30.<\/p>\n

\"M\u00ebsimi
\n
\n3. Ne kemi nj\u00eb kurb\u00eb aproksimuese mbi pik\u00ebt e m\u00ebsimit, shohim gabimin q\u00eb ajo jep n\u00eb t\u00eb dh\u00ebnat kontrolluese.<\/p>\n

\"M\u00ebsimi
\n
\n4. Ne kemi nj\u00eb kurb\u00eb t\u00eb sakt\u00eb p\u00ebrmes pik\u00ebve t\u00eb m\u00ebsimit, dhe shohim nj\u00eb gabim monstruoz n\u00eb t\u00eb dh\u00ebnat kontrolluese (dhe zero n\u00eb ato t\u00eb m\u00ebsimit, por \u00e7far\u00eb vlere ka?).<\/p>\n

\"M\u00ebsimi
\n
\nK\u00ebtu \u00ebsht\u00eb treguar, natyrisht, varianti m\u00eb i thjesht\u00eb me nj\u00eb ndarje t\u00eb vetme n\u00eb n\u00ebn-grupet e \"m\u00ebsimit\" dhe \"kontrollit\", por n\u00eb rastin t\u00eb p\u00ebrgjithsh\u00ebm kjo b\u00ebhet shum\u00eb her\u00eb p\u00ebr p\u00ebrshtatjen m\u00eb t\u00eb mir\u00eb t\u00eb koeficient\u00ebve.<\/p>\n

Skedari \u00ebsht\u00eb i disponuesh\u00ebm k\u00ebtu, i verifikuar nga antivirus.<\/a><\/noindex> Aktivizoni makros p\u00ebr pun\u00eb t\u00eb sakt\u00eb.<\/p>\n

3. Zbritja gradient dhe dinamika e ndryshimit t\u00eb gabimit.<\/h2>\n

\nK\u00ebtu do t\u00eb jet\u00eb rasti 4-dimensionale dhe regresioni linear. Koeficient\u00ebt e regresionit linear do t\u00eb p\u00ebrcaktohen hap pas hapi me metod\u00ebn e zbritjes gradient, fillimisht t\u00eb gjith\u00eb koeficient\u00ebt jan\u00eb zero. N\u00eb nj\u00eb grafik t\u00eb ve\u00e7ant\u00eb duket dinamika e uljes s\u00eb gabimit nd\u00ebrsa koeficient\u00ebt p\u00ebrshtaten m\u00eb sakt\u00eb. Ka mund\u00ebsi p\u00ebr t\u00eb par\u00eb t\u00eb gjitha kat\u00ebr projekcionet 2-dimensionale.<\/p>\n

N\u00ebse caktojm\u00eb nj\u00eb hap shum\u00eb t\u00eb madh t\u00eb zbritjes gradient, at\u00ebher\u00eb duket se \u00e7do her\u00eb do t\u00eb shkelim minimumin dhe p\u00ebr t\u00eb arritur rezultatin do t\u00eb na duhen m\u00eb shum\u00eb hapa, megjithat\u00eb, p\u00ebrfundimisht do t\u00eb arrijm\u00eb (n\u00ebse vet\u00ebm nuk e ngrisim shum\u00eb hapat e zbritjes - pastaj algoritmi do t\u00eb shkoj\u00eb \"n\u00eb d\u00ebshtim\"). Dhe grafiku i var\u00ebsis\u00eb s\u00eb gabimit nga hapi i iteracionit do t\u00eb jet\u00eb jo i qet\u00eb, por \"i trazuar\".<\/p>\n

1. Generojm\u00eb t\u00eb dh\u00ebna, caktojm\u00eb hapin e zbritjes gradient.<\/p>\n

\"M\u00ebsimi
\n
\n2. Me zgjedhjen e duhur t\u00eb hapit t\u00eb zbritjes gradient, arrijm\u00eb ngadal\u00eb dhe mjaft shpejt te minimumi.<\/p>\n

\"M\u00ebsimi
\n
\n3. Me zgjedhjen e gabuar t\u00eb hapit t\u00eb zbritjes gradient, kalojm\u00eb maksimumin, grafiku i gabimit \u00ebsht\u00eb \"i trazuar\", konvergjenca k\u00ebrkon m\u00eb shum\u00eb hapa.<\/p>\n

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

\"M\u00ebsimi
\n
\n4. Me zgjedhjen krejt\u00ebsisht t\u00eb gabuar t\u00eb hapit t\u00eb zbritjes gradient, largohemi nga minimumi.<\/p>\n

\"M\u00ebsimi
\n
\n(P\u00ebr t\u00eb riprodhuar procesin me vlerat e treguara n\u00eb figurat e hapit t\u00eb zbritjes gradient, vendosni shenj\u00ebn \"t\u00eb dh\u00ebna referimi\").<\/p>\n

Skedari \u00ebsht\u00eb n\u00eb k\u00ebt\u00eb lidhje, duhet aktivizuar makros, nuk ka viruse.<\/a><\/noindex><\/p>\n

Si\u00e7 mendon komuniteti i nderuar, a \u00ebsht\u00eb e pranueshme nj\u00eb thjeshtesim i till\u00eb dhe metoda e prezentimit t\u00eb materialit? A duhet ta p\u00ebrkthejm\u00eb artikullin 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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