Rhoqo ekupheleni konyaka, ecaleni kwexesha leeholide zolonwabo kunye nethemba lonyaka omtsha, olunye “ulonwabo” ngoku olubonakala sisiko olusekelwe kwimibhiyozo yale mihla yoNyaka oMtsha luqikelelo lonyaka ozayo. Ukusuka kwiingqondo ukuya iingcali zoshishino, abantu bayachukunyiswa kuko konke oko kuxelwe kwangaphambili, belangazelela ukufunda ngekamva elizayo.
Ngokufanayo, kwihlabathi elizaliswe yidatha ngoku, i-Statistical Forecasting-indlela yenzululwazi yokuhlalutya idatha yembali ukuqikelela intsingiselo yexesha elizayo kwiinkalo ezahlukeneyo, nokuba yimali kunye nezoqoqosho okanye ikhonkco lokubonelela kunye ne-logistics-idla ngokuba yi-linchpin ekuqikeleleni ikamva lezoshishino. Nangona kunjalo, ngokuhambela phambili kokuFunda koMatshini, izinto ziya ziba nomdla ngakumbi kule mihla njengoko zifaka ukungcangcazela kunye nobunzulu kuQoqosho loBalo.
Qhubeka ufunda ukuze ufumane unxibelelwano phakathi kokuFunda koMatshini kunye noQingqo-manani loBalo, usetyenziso lweSifundo soShishino kuQoqosho loBalo namhlanje, kunye nendlela uKufunda ngoomatshini kunokuphucula uQeqesho loBalo.
Isiqulatho
1. Intshayelelo yokuFunda koomatshini kunye nokuqikelelwa kwamanani
2. Izicelo ezingundoqo zokuFunda koomatshini kwi-Statistical Forecasting
3. Indlela uFundo ngoomatshini oluphucula ngayo uQeqesho loBalo
4. Uqikelelo oluchanekileyo olunokufikelelwa
Intshayelelo yokuFunda koomatshini kunye noQeqesho loBalo
Amagqabantshintshi amafutshane ngokuFunda koomatshini
Ukufunda ngoomatshini, iseti yobukrelekrele bokwenziwa kunye nesayensi yekhompyuter, yenza ukuba i-AI ifunde ngendlela efana nokufunda komntu. Ngokusebenzisa i-algorithms kunye nedatha, ukufundwa koomatshini kuhlala kuphucula ukuchaneka kwayo. Ukukwazi ukucubungula kunye nokuhlalutya inani elikhulu ledatha, ukufumana iipateni, kunye nokwenza uqikelelo lwezinye zeempawu zayo ezibalaseleyo. Ngokomzekelo, malunga nesiqingatha se top 10 izicelo zokufunda koomatshini Okubalulekileyo kubo bobabini abachwephesha bamashishini kunye nabasebenzisi bemihla ngemihla banxulumene noqikelelo kunye noqikelelo, kubandakanya iimeko zetrafikhi, ukhetho lwabathengi, kunye nokuhamba kwesitokhwe.
Ngokwenene, ukufundwa koomatshini kubonisa ukuqhubela phambili okubonakalayo kwitekhnoloji yokuqikelela njengoko inokuqonda iipateni ezinzima kunye nobudlelwane kwidatha enokuthi ingabonakali kubahlalutyi babantu. Ikwanakho ukuphuculwa okuqhubekayo koqikelelo ngokuhamba kwexesha, ukucokisa ukusebenza kwayo njengoko idatha eninzi ilungiswa.
Iziseko zoQoqosho loBalo
Uqikelelo-manani isebenzisa idatha yembali edityaniswe neendlela zobalo ezibandakanya iisampulu kunye novavanyo lwe-hypothesis ukuchonga iintsingiselo, iipateni zamaxesha onyaka kunye nolungelelwaniso. Ezi zinto zisetyenziselwa ukwenza umzekelo wobudlelwane kunye nokuqikelela iziphumo zexesha elizayo. Le ndlela isebenza phantsi kwengcinga yokuba iipatheni zexesha elizayo ziya kubonisa ezidlulileyo.
Iindlela zobalo ezifana nohlahlelo lwexesha kunye neemodeli zokuhlehla zixhaphakile kwaye zisetyenziswa ngokubanzi kwiinkalo ezahlukeneyo. Ngokuhlalutya idatha edlulileyo, le mizekelo ichaza kwaye iqikelele iziganeko zexesha elizayo kunye namanqanaba ahlukeneyo okuchaneka, kuxhomekeke ekucaceni kunye nokuhambelana kweepatheni zedatha.
Kulawulo lwesixokelelwano sobonelelo, uQingqo-manani loBalo lubalulekile kucwangciso lwemfuno, ulawulo lwempahla, kunye nolwabiwo lwezibonelelo, kuphuculwe iinkqubo zekhonkco lobonelelo. Uthotho lwexesha ubuchule bokuxela kwangaphambili njenge I-ARIMA (i-Autoregressive Integrated Moving Average) kunye ne-exponential smoothing ziyinxalenye ebalulekileyo yale ndlela, ngokujonga ukomelela kwazo kuhlalutyo lwentsingiselo kunye nohlengahlengiso lwamaxesha onyaka. Ukubeka nje ngokulula, uQingqo-manani loBalo lusebenza njengelitye lembombo kucwangciso lobuchule kunye nokusebenza kakuhle kolawulo lwekhonkco lonikezelo, ukusebenzisa iindlela ezivavanywe ixesha ukomeleza ukuqikelela.
Ukufunda ngoomatshini kunye noQeqesho lwamanani: Indaleko kunye nobudlelwane
Ulawulo lwesixokelelwano sobonelelo lwangoku ludibanisa iinkcukacha-manani ezongezelelekileyo kunye neemodeli zokuFunda ngoomatshini ukujongana nobunzima bolungelelwaniso lwehlabathi, imithombo yolwazi eyahlukeneyo, kunye nokuhamba kwedatha okwexesha lokwenyani. Olu tshintsho lusuka kwiindlela zobalo lwemveli ukuya kwingqikelelo eqhutywa nguMatshini yokuFunda lubonisa ukuguquka okubalulekileyo, kuqaqambisa amandla olawulo lwekhonkco lokubonelela ngokwamkela iinkqubo zokwenza izigqibo ezinamandla, eziqhutywa yidatha.
Ekuqaleni, uphando kunye nokhuphiswano ucebise ukuba iindlela zochungechunge lwexesha leklasiki ziphezulu kuneendlela zokuFunda ngoMatshini ekuqikeleleni ukuchaneka. Nangona kunjalo, i 2020 ukhuphiswano lweM5 ibonise isakhono sokuFunda ngoomatshini, ngelixa isakhela phezu kweziseko zoqikelelo lwamaxesha akudala, ibambe yaze yagqitha kwiindlela zakudala.
Ukuza kuthi ga ngoku, ukuFunda ngoomatshini kusanda kudityaniswa noQeqesho loBalo kwaye kusetyenziswa ecaleni kweendlela zobalo ukuphucula ukusebenza ngokupheleleyo. Le ndlela ayithethi malunga nokutshintsha iindlela zemveli ngokuthe ngqo; kunoko, izalisekisa ezo ndlela ngokuziqhelanisa nokuphucula uqikelelo ngokuhamba kwexesha, okubalulekileyo kwimo engqongileyo eguquguqukayo kwikhonkco lonikezelo.
Ukuvela koqikelelo kubone iimodeli zokuFunda koMatshini zivelela ngakumbi, zicela umngeni kulawulo lweendlela zobalo zemveli njenge-ARIMA kunye ne-Exponential Smoothing. Ngamandla abo ekuqhubeni nasekuhlalutyeni iiseti zedatha ezinkulu, ukutyhila iipateni ezinzima, kunye neendlela zokuqikelela, ii-algorithms zokuFunda ngoomatshini zibonelele ngophuculo olubalulekileyo ekuququzeleleni izigqibo eziqhutywa yidatha, ngokuqhubekayo nokucokisa uqikelelo.
Izicelo ezingundoqo zokuFunda koomatshini kuQoqosho loBalo
Kwimixokelelwane yokubonelela, ukuFunda koMatshini kuphucula ukuqikelela kwemfuno ngokusebenzisa ubuchule obunje Ukuva imfuno, ibalulekile ekucwangciseni nasekuphuculeni ulungiselelo noluhlu lwempahla. Esi sicelo siphucula ulawulo lwe-inventri ngokuqikelela ukuziphatha kwabathengi kunye neentsingiselo zemarike, ukunciphisa i-overstock okanye ukungabikho kwamasheya, kunye nokwenza uhlalutyo lwexesha lokwenyani.
Ukufunda ngoomatshini kwaziwa ngobuchule bayo obuphezulu bokuphatha idatha enomgangatho ophezulu ngaphandle kokucaciswa kwemodeli yokuqala. Iyagqwesa ekusetyenzweni kwedatha eyahluka-hlukeneyo, ibandakanya ukungahambelani kunye nokubhaqwa okungaqhelekanga, ngaloo ndlela ixabiseke kakhulu kwimisebenzi yokuqikelela enzima efana ukuqondwa komfanekiso kunye nobhaqo lobuqhetseba, apho iindlela zamanani eziqhelekileyo zidla ngokuba nzima.
Ngokusisiseko, ukuFunda koomatshini kuyakwazi ukuchonga ezi patheni zintsonkothileyo ngokusebenzisa izigaba zokufunda eziqhutywa luthungelwano lwe-neural, ukwandisa amandla okuqikelela ngaphaya kwezo zemodeli zemveli. Ngokusebenzisa iindlela ezahlukeneyo ze-algorithms, ukuFunda ngoomatshini kunika ukuphuculwa kokulungiswa kwedatha kunye nokuguquguquka kwimeko yedatha enzima. Oku kuphucula amandla eendlela zokuqikelela zemveli kwaye kuqhuba ukuthathwa kwezigqibo eziqhutywa yidatha.
Indlela yokuFunda ngoomatshini kuluphucula ngayo uQeqesho loBalo
Ukuchaneka koqikelelo oluphuculweyo
Ukufunda ngoomatshini kukhulisa kakhulu ukuchaneka koqikelelo ngokukwazi kwayo ukusetyenzwa nokuhlalutya iiseti zedatha ezinkulu nezintsonkothileyo, ngokuqhubekayo ukuziqhelanisa nedatha entsha ukucokisa uqikelelo. Kwikhonkco lokubonelela, ukuchaneka koqikelelo olunjalo kuchaphazela ngokuthe ngqo ukusebenza kakuhle ngokunciphisa isitokhwe esigqithisileyo kunye nokuphela kwamasheya, ukuqinisekisa ulawulo olungcono lwe-inventri.
Ukongeza, kuye kwangqinwa ukuba Iimodeli zokuFunda ngoomatshini okufana neHlathi elingeQobo, uMatshini wokuNyusa iGradient Light (LightGBM), kunye ne-eXtreme Gradient Boosting (XGBoost) ziphucula ukuchaneka koqikelelo ngoqikelelo oludityanisiweyo olusuka kwiimodeli ezininzi. Ezi modeli, zisetyenziswa kwiindlela ezidityanisiweyo, zidibanisa uqikelelo kwiimodeli ezahlukeneyo ezilula, ziphucula kakhulu ukuchaneka koqikelelo. Bafezekisa oku ngokusebenzisa utoliko lwedatha olwahlukeneyo kunye nokunciphisa ukufakwa ngokugqithisileyo, ngaloo ndlela begqitha ngeendlela eziqhelekileyo zokuqikelela.
Ngaphaya koko, iimodeli zokuFunda ngoomatshini ezifana ne-XGBoost kunye neeNethiwekhi zeMemori yeXesha elifutshane (LSTM) zibonisiwe. ukusebenza okuphezulu kunezibalo zemveli iimodeli ngokubamba iipateni ezintsonkothileyo kwidatha eziphucula kakhulu ukuchaneka koqikelelo. Le mifuziselo yokuFunda ngoomatshini ibonelela ngoqikelelo oluchaneke ngakumbi ngokuchonga iipateni ezintsonkothileyo eziqhele ukuqondwa ngeendlela eziqhelekileyo.
Kwelinye icala, a umzekelo wokuphunyezwa ibonisa ukuba inkqubo yokuhlelwa kwamanyathelo amabini isebenzisa imithi yesigqibo kunye ne-multitask neural networks yenza ukuba i- Machine Learning igqibe ngokufanelekileyo imodeli yeenkcukacha-manani kunye neeparamitha zayo zochungechunge lwexesha le-SKU. Le ndlela ibonisa indlela uqikelelo olunokusebenzisa ngayo iindlela zokufunda eziphucukileyo ukulungelelanisa ngobuchule ezona modeli zobalo zifanelekileyo nezingakhethi cala kunye neentsingiselo ezithile zemfuno, ngokukodwa ukuphucula indlela iFundo ngoMatshini kunye neendlela zokuqikelela zemveli zisebenza kunye.
I-granularity kunye nokuguquguquka
Uqikelelo lwamanani ngokwendalo luxhomekeke kwindlela yalo ebanzi yokuvavanya nokuhlalutya idatha yembali yexesha elide. Ngenxa yoko, iqhele ukusetyenziselwa uqikelelo kwixesha elide kunokuguquguquka kwexesha elifutshane. Ngokukodwa, i-micro-forecasting ngokwesiko ibingengomandla ayo. Ngokwahlukileyo, ukuguquguquka okuguquguqukayo kweemodeli zokuFunda koMatshini, ngakumbi abo baqeshe ukufunda kwi-Intanethi, kubavumela ukuba bahlengahlengise ngokukhawuleza idatha entsha.
Esi sikhundla sixhasa uqikelelo oluthe kratya lwegranular kunye nemeko ethile, ukulungelelanisa uqikelelo lweemeko zentengiso yexesha langempela. Ukuguquguquka okunjalo kubonakaliswa kwi Ukuva imfuno, ukuphucula ukukwazi ukubonelela ngoqikelelo oluthe kratya lwegranular noluguquguqukayo, olubalulekileyo kwiimpendulo ezikhawulezayo zokuphazamiseka kwekhonkco lokubonelela okanye utshintsho kwimfuno yabathengi.
Ngokubanzi, ukuFunda ngoomatshini kuphucula ukubikezelwa kwe-micro-forecasting kwikhonkco lokubonelela ngokuziqhelanisa notshintsho lwexesha lokwenyani. Iphatha ngokufanelekileyo kwaye ilungelelanise uluhlu olukhulu lweepateni zedatha ezintsonkothileyo, ezahlukeneyo, kunye neziguquguqukayo, ilungiselela ngakumbi uluhlu kunye nokunciphisa inkunkuma. Ngenxa yoko, ukuFunda ngoomatshini kume njengesixhobo esibalulekileyo kulawulo lwesixokelelwano sanamhlanje, esenza ukuba amashishini asabele ngokukhawuleza nangokufanelekileyo kutshintsho lwentengiso kunye nokuguquguquka kwemfuno.
Ukusebenza kakuhle kweendleko
Impembelelo yokuFunda koMatshini ekuphuculeni ukusebenza kakuhle kweendleko kuqikelelo lwamanani kunxulunyaniswa ngokusondeleyo namandla ayo okuphucula ukuchaneka. Ngokuzenzekela iinkqubo zokuhlalutya idatha, ukuFunda ngoomatshini akunyusi nje ukuchaneka koqikelelo kodwa kwakhona kunciphisa kakhulu iindleko zokuxela kwangaphambili, okubalulekileyo kwimisebenzi yokubonelela ngezinto ezinkulu.
Ukusasazwa kwe meta-learning, ngenye indlela ebizwa ngegama "ukufunda ukufunda ngokukhawuleza", i-subset of Machine Learning, iphinda ikhulise ukusebenza kwe-algorithms yokufunda ngokwenza uhlengahlengiso ngokusekelwe kwiziphumo zovavanyo. Le ndlela inceda ukucutha ngakumbi iindleko zokubala ngokunciphisa imfuno yokukhangela okupheleleyo kwiimodeli ezininzi zoqikelelo kunye neeparamitha ngexesha lomjikelo ngamnye woqikelelo, ngaloo ndlela kulondolozwa ixesha kunye nezibonelelo kunye nokuphucula ngokumangalisayo ukusebenza kakuhle kweendleko.
Ukujonga ngokwembono ebanzi, ukuFunda ngoomatshini kukwanegalelo ekuncitshisweni kweendleko ezinxulumene ne-inventri egqithisileyo kunye neentengiso ezilahlekileyo. Umzekelo, ukuFunda ngoomatshini kunciphisa iindleko ezinxulunyaniswa nokugcwala okanye ukuthoba isitokhwe sangaphantsi ngoqikelelo oluchaneke ngakumbi lwemfuno. Ngaphaya koko, ngokufunyanwa kwezinto ezizenzekelayo, iimodeli zokuFunda ngoomatshini zikwanciphisa imfuneko yobunjineli beempawu zezandla, ezinokukhokelela ngokungathanga ngqo kunciphiso lweendleko kwisigaba sophuhliso lwemodeli.
Inkxaso yesigqibo
Iimodeli zokuFunda ngoomatshini, ezikwaziyo ukuhlalutya ngokukhawuleza nangokuchanekileyo iiseti zedatha ezinkulu nezintsonkothileyo, zibonelela ngenkxaso yesigqibo esinamandla kwiindawo eziguquguqukayo. Ezi modeli zongeza iinkqubo zokwenziwa kwezigqibo apho uqikelelo olukhawulezayo noluchanekileyo lubalulekile, lunika ulwazi olunzulu kunye noqikelelo oluthembeke ngakumbi. Kulawulo lwekhonkco lokubonelela, ngokukodwa, uqikelelo olunjalo lwangexesha lugxininisa ukubaluleka kokwenziwa kwezigqibo ezingcono zokuthengwa nokuhanjiswa kwezicwangciso-izinto ezibalulekileyo ekulawuleni ulungiselelo ngokufanelekileyo.
Ngaphezulu koko, ukuFunda ngoomatshini kuxhobisa abenzi bezigqibo ngababikezeli kunye nezixhobo ezichonga ngokukhawuleza ezona ndlela zisebenzayo zokuqikelela, ukuphucula ukuqonda kwabo iipateni zemfuno kunye neziphumo zengqikelelo. Oku kubavumela ukuba bagxile ekusulungekiseni uqikelelo lwengqikelelo apho kubaluleke kakhulu, ukukhulisa inkqubo yesicwangciso sobuchule ngokubanzi.
Okokugqibela, inqaku elibalulekileyo nelingenakuphikiswa lokuFunda koMatshini ekuxhaseni ukwenziwa kwezigqibo kukukwazi ukuqinisekisa uqikelelo olungakhethi cala olusekwe kuphela kwidatha. Le yinzuzo ethile yokuFunda ngoomatshini: ivelisa uqikelelo oluqhutywa kuphela yidatha, engenamkhethe ebantwini, kwiimvakalelo, okanye ekutolikweni okuzimeleyo.
Ngelixa i-Statistical Forecasting iphinda ixhomekeke kwidatha kunye neendlela zobuninzi, amandla okuxhatshazwa kwabantu ngexesha lokukhetha imodeli, ukusetwa kweparamitha, kunye nokutolikwa kweziphumo kunokuchaphazela iziphumo ukuya kwinqanaba elithile. Ukufunda ngoomatshini, ngakumbi ngeenkqubo ezizisebenzelayo kunye neziguquguqukayo, kunciphisa oku ngokusetyenzwa kwedatha eninzi ngokuqhubekayo nangokuguquguqukayo ekuphenduleni ulwazi olutsha. Lo gama nje idatha ngokwayo ingenamkhethe, le ndlela inokukhokelela ekuqikeleleni okunenjongo kunye nokungakhethi cala.
Uqikelelo oluchanekileyo olufikelelekayo
Ukudityaniswa kokuFunda koMatshini kuqikelelo lwamanani kubonisa ukuqhubela phambili okubonakalayo kuhlalutyo lokuxela kwangaphambili. Le ndibaniselwano iphucula ukuchaneka koqikelelo ngelixa ikwazisa into engazange ibonwe ngaphambiliinqanaba lokuziqhelanisa kunye nokuchaneka ekuphatheni iiseti zedatha ezahlukeneyo kunye neemeko zokuqikelela. Ii-algorithms zokuFunda ngoomatshini zinegalelo ekudaleni uqikelelo oluchanekileyo ekufikeleleni ngokuvumela iinkqubo zokuthatha izigqibo ezisulungekisiweyo nezinolwazi, zombini zibalulekile kwiimfuno eziguquguqukayo zamashishini anamhlanje.
Iinzuzo zokuFunda ngoomatshini zandisa ngaphaya kokuchaneka; zibandakanya ukuphuculwa kweendleko kunye nokukwazi ukusebenzisa iiseti zedatha ezibanzi ngokufanelekileyo. Ezi nkqubela phambili ziququzelela ukuqonda okunzulu kweentsingiselo zemarike kunye nokuziphatha kwabathengi, ngaloo ndlela kuxhasa ukuthathwa kwezigqibo ezicwangcisiweyo kumacandelo awohlukeneyo. Ngokufunda ngoomatshini, imibutho ixhotyiselwe ukwenza izigqibo ezinolwazi ngakumbi, ukwenza ngcono imisebenzi kunye nokunciphisa imingcipheko enxulumene nokuxela kwangaphambili okungalunganga. Ikamva loqikelelo lwengqikelelo limiselwe ukufezekisa ukudityaniswa okukhulu ngakumbi kobu buchwepheshe, ngokuqhubekayo ukuphucula umda kunye nokuchaneka kohlalutyo lokuxela kwangaphambili.
Fumanisa indlela ukuFunda ngoomatshini kulutshintsha ngayo uqikelelo kumashishini ngokundwendwela rhoqo I-Chovm.com ifundeka-Isixhobo esibalulekileyo sokuqonda kweshishini, iindaba, kunye nezicwangciso ezinokwenza umahluko.
Ngaba ujonge isisombululo solungiselelo kunye namaxabiso okukhuphisana, ukubonakala okupheleleyo, kunye nenkxaso yomthengi efikelelekayo? Jonga i Chovm.com Logistics Marketplace namhlanje.
