Ingabe usukulungele ukuhamba uhambo olujabulisayo endaweni yobuhlakani bokwenziwa?
Akukaze kube nethuba elingcono lokuhlola izinhlaka ze-AI, ngenxa yokukhula kobuhlakani bokwenziwa.
Kunezixazululo ezimbalwa ezitholakalayo, kusukela ku-TensorFlow ne-PyTorch kuya e-Keras naseCaffe. Kuye ngezinhloso zakho, uhlaka ngalunye lunezinzuzo ezihlukile kanye nokubi.
Ngakho-ke, noma ungumuntu osanda kuzalwa noma unjiniyela onolwazi, ake siqale futhi sibheke izinhlaka ze-AI ezinhle kakhulu ezitholakala namuhla.
1. I-PyTorch
I-PyTorch iwuhlaka oluqinile lokufunda komshini ovulekile olushanele umphakathi we-AI kusukela ekuqaleni kwawo ngo-2016. Isiphenduke uhlaka oluya ohlakeni ngokushesha ngenxa yenethiwekhi yayo yokubala enamandla kanye nesixhumi esibonakalayo esisebenziseka kalula.
Kodwa yini ehlukanisa i-PyTorch esixukwini? Okokuqala, ineqoqo eliqinile lamakhono. Lokhu kuyenza iphelele ekwakheni nasekusebenziseni amamodeli wokufunda wemishini.
I-PyTorch iwuhlaka olulungele ukukhiqiza olungathenjwa ngisho nasezinhlelweni zokusebenza ezinzima kakhulu, ngenxa yoshintsho olushelelayo phakathi kwezindlela ezishisekelayo nezegrafu zisebenzisa i-TorchScript kanye nekhono layo lokusheshisa indlela eya ekukhiqizeni kusetshenziswa i-TorchServe.
Ukwengeza, i-PyTorch ine-ecosystem ephelele yamathuluzi nemitapo yolwazi. Lawa mathuluzi asiza ekwakhiweni kwe umbono wekhompyutha, NLP, nezinye izinhlelo zokusebenza.
Ibuye isekelwe kakhulu kumapulatifomu amakhulu amafu, okuvumela ukuthuthukiswa okulula nokukala.
buhle
- I-TorchScript ikuvumela ukuthi ushintshe kalula phakathi kwamamodi alangazelelayo nawegrafu, kuyilapho i-TorchServe isheshisa uhambo lokukhiqiza.
- I-ecosystem eqinile yamathuluzi nezinhlaka inweba i-PyTorch futhi ivumela ucwaningo ekuboneni ngekhompyutha, ukucutshungulwa kolimi lwemvelo, nezinye izindawo.
- Amapulatifomu amakhulu amafu asekelwa kahle, avumela ukuthuthukiswa okungenazingxabano nokukala okulula.
bawo
- Uma kuqhathaniswa nezinye izinhlaka, inomphakathi omncane othuthukayo.
- Kukhona ukuntuleka kwamathuluzi okuqapha nawokubona ngeso lengqondo, njengebhodi le-tensor.
2. UKeras
Ingabe ukhathele ukukhubeka ngenxa yama-API adidayo nemilayezo yephutha lapho uthuthukisa amamodeli okufunda omshini? Ungabheki kude kuneKeras, a uhlaka lokufunda olujulile eyenzelwe abantu kunamarobhothi.
I-Keras igcizelela ubulula, ukusebenziseka kalula, kanye nemibhalo ephelele. Lokhu kukwenza kube ukukhetha okudumile phakathi konjiniyela abazama ukudala nokusebenzisa imikhiqizo esebenza ngomshini yokufunda.
Kodwa akupheleli lapho: I-Keras ine-ecosystem ebanzi yamathuluzi nezinsiza ezimboza yonke ingxenye yokuhamba komsebenzi wokufunda komshini.
Ngokuvumelana nezimo ukuze usebenzise amamodeli e-Keras yonke indawo, kusukela kusiphequluli kuye kumadivayisi eselula kuya kumasistimu ashumekiwe, ungasebenzisa ngokugcwele amandla we-TensorFlow kunoma yisiphi isimo.
buhle
- Yakhiwe ngama-API alula kanye nemibhalo ephelele ukuze kube lula ukusetshenziswa komuntu.
- Ilungiselelwe ngokwedlulele isivinini sokulungisa iphutha, ubuhle bekhodi, nokusebenziseka
- Kulinganiswa kalula kumaleveli e-axascale ngenxa yokusebenzisana neplathifomu ye-TensorFlow
- Izinketho eziningi zokusebenzisa, kusukela kuziphequluli kuye kumadivayisi eselula kuya kumasistimu ashumekiwe
bawo
- Iguquguquka kancane kunezinye izinhlaka zokufunda ezijulile
- Ezimweni ezithile zokusetshenziswa eziyinkimbinkimbi, kungase kudingeke imitapo yolwazi eyengeziwe noma amathuluzi
- Akwaziwa kakhulu noma okuvame ukusetshenziswa njengezinye izinhlaka
3. I-TensorFlow
Dala amamodeli okufunda omshini afanele ukukhiqizwa usebenzisa i-TensorFlow! I-TensorFlow ikunikeza izinsiza ozidingayo ukuze uthuthukise eyakho amaphrojekthi wokufunda ngomshini, kungakhathaliseki ukuthi ungusosayensi wedatha noma usanda kufika.
Kungakhathaliseki izinga lakho lolwazi, ungavele uqalise nge-TensorFlow ngenxa yamamodeli aqeqeshwe kusengaphambili nezifundiso ezifinyelelekayo.
I-TensorFlow ayiwona nje umtapo wolwazi wokufunda ngomshini. Kuyinkundla yokufunda yomshini ephuma ekupheleni enikeza izinketho zesinyathelo ngasinye senqubo yakho, kusukela ekusetshenzisweni kwemodeli kuya ekulungiseni idatha.
I-TensorFlow yenza kube lula ukuphakela amamodeli akho yonke indawo, noma ngabe wakha uhlelo lokusebenza lwewebhu, uhlelo lokusebenza leselula, noma idivayisi eshumekiwe.
buhle
- Inkundla ebanzi yokufunda komshini kusukela ekuqaleni kuya ekugcineni
- I-Scalable futhi iyavumelana nezimo
- Itholakala ezinguqulweni eziningana ezimweni ezihlukahlukene zokusetshenziswa
- I-ecosystem enkulu enezinsiza zomphakathi namamodeli aqeqeshiwe
bawo
- Kukhona umqansa wokufunda kulabo abasanda kuqala
- Idinga inani elithile lobuchwepheshe nokuqonda.
4. Caffe
Uhlaka lokufunda olujulile olubizwa nge-Caffe lwadalwa ngokugxila esivinini kanye ne-modularity.
Ngenxa yobulula bayo bokusebenzisa nokucubungula idatha ngokushesha, iCaffe, eyakhiwe yiBerkeley Vision and Learning Center (BVLC), isizuze ukuduma kubacwaningi namabhizinisi.
Kungenye indlela ekhangayo yabantu abadinga ukuqeqesha futhi basebenzise amamodeli kuhlu lwehadiwe ngenxa yedizayini yayo esebenza kahle kakhulu, eyivumela ukuthi isebenze kuwo womabili ama-CPU nama-GPU.
buhle
- Kuyashesha futhi kusebenza ngempumelelo.
- I-Caffe ivumelana nezimo nge-architecture ye-modular.
- Usizo oluhle lomphakathi luyatholakala.
bawo
- Kungase kungabi inketho efanelekile yezinhlelo zokusebenza eziyinkimbinkimbi ngenxa yamandla ayo anomkhawulo.
- Ngokungafani nezinye izinhlaka, hhayi njengokusebenziseka kalula
- Idinga ulwazi oluthile lokuhlela.
5. I-MXNet
Uhlaka lokufunda olujulile i-MXNet lwadalwa ngokusebenza kahle nokuvumelana nezimo engqondweni. Ungakha kalula futhi usebenzise amanethiwekhi we-neural ngesixhumi esibonakalayo esisebenziseka kalula ngezinhloso eziningi.
Ithuthukiswa kucatshangelwa izimo zokusetshenziswa kokukhiqiza, okuhlanganisa amakhono afana nokuhlola imodeli, ukunikeza amamodeli, nosekelo lwefomethi ye-ONNX. Lokhu kwenza kube lula ukusebenzisa amamodeli akho ezindaweni ezimbalwa, okuhlanganisa amadivayisi ashumekiwe kanye nezindawo zamafu.
Ezinye izici namathuluzi ahlinzekwe yi-MXNet ahlanganisa izilayishi zedatha ezakhelwe ngaphakathi, amamodeli aqeqeshwe kusengaphambili, nosizo lokuhlukanisa ngokuzenzakalelayo. Ukufunda okujulile ochwepheshe bawo wonke amazinga wamakhono bavame ukuyikhetha ngenxa yomphakathi wayo onempilo kanye nemibhalo ephelele.
buhle
- I-Scalable: I-MXNet inketho enhle kakhulu yezinhlelo zokusebenza ezinkulu njengoba isekela ukuqeqeshwa okusabalalisiwe kuma-GPU amaningi nama-CPU.
- I-MXNet ilula ukuyifaka ezinqubweni zamanje njengoba isekela izilimi ezihlukahlukene zekhompiyutha, okuhlanganisa iPython, R, Julia, Scala, Perl, ne-C++.
- Iyahambisana neLinux, Windows, macOS, iOS, ne-Android.
bawo
- I-MXNet inejika lokufunda eliphezulu futhi ingadinga isikhathi esithile ukuze ibe yingcweti, efana nenye izinhlaka zokufunda ezijulile.
- Okungadumile kangako: Nakuba i-MXNet yamukelwa, ayikasetshenziswa njalo njengezinye izinhlaka zokufunda ezijulile njenge-TensorFlow noma i-PyTorch, ephakamisa ukuthi kungase kube nezinsiza ezimbalwa zomphakathi ezifinyelelekayo.
6. Theano
Ikhithi yamathuluzi yokubala yezinombolo eqinile ebizwa ngokuthi i-Theano inika abasebenzisi amandla okuklama, ukuthuthukisa, nokuhlola izinkulumo zezibalo ngempumelelo. Inikeza isikhombimsebenzisi esiqondile sokwenza imisebenzi yezibalo kudathasethi enkulu futhi ithuthukiswa phezu kwePython.
Ukuvumelana nezimo kukaTheano ukwenza izibalo kuwo womabili ama-CPU nama-GPU kungenye yezinzuzo zakhona eziyinhloko. Lokhu kuyenza ilungele izinhlelo zokusebenza zokufunda ezijulile ezidinga ukucutshungulwa kokusebenza okuphezulu.
Ngaphezu kwalokho, i-Theano inikeza amakhono ahlukahlukene wokuthuthukisa abasebenzisi abangawasebenzisa ukuze bathuthukise ukusebenza nokunemba kwamamodeli wabo.
Manje, ake sihlole ubuhle nobubi bayo.
buhle
- I-Theano iphumelela ngendlela emangalisayo ekwenzeni izibalo zezinombolo njengoba yakhelwe ukuthuthukisa igrafu yokubala yezinkulumo zezibalo.
- Kuwuhlaka oluvumelana nezimo kakhulu.
- Izinhlelo zokusebenza zokufunda okujulile ezisebenza kahle zizuza kakhulu ekusebenziseni i-GPU eqinile ye-Theano. Yakhelwe ukusebenza kalula ngama-GPU.
bawo
- Labo abangayazi i-Python noma amanye amalabhulali wokubala amanani bangase bakuthole kuyinselele ukufunda i-Theano.
- I-Theano ingase ingasakwazi ukuthola izibuyekezo noma ama-bug patches ngoba ukuthuthukiswa kwayo kwehlile kamuva nje.
- Amadokhumenti anganele: abanye abasebenzisi bangase bakuthole kuyinselele ukusebenzisa i-Theano njengoba imibhalo yayo inemininingwane encane kunaleyo yemitapo yolwazi eqhudelanayo yokubala izinombolo.
7. I-Microsoft Cognitive Toolkit
Ake sibheke i-Microsoft Cognitive Toolkit, uhlaka lwamahhala nomthombo ovulekile lokuthuthukisa amamodeli okufunda ajulile. Ihloselwe ukuqeqesha amamodeli amakhulu kuma-GPU ambalwa nemishini.
I-Cognitive Toolkit iyinketho edumile phakathi kososayensi bedatha nabacwaningi bokufunda ngomshini nge-API yayo esebenziseka kalula kanye namakhono okuqeqesha asabalalisiwe amahle kakhulu.
Esinye sezici ezibalulekile ze-Cognitive Toolkit yikhono layo lokuqeqesha nokuphakela amamodeli ezinhlobonhlobo zehadiwe, okuhlanganisa ama-CPU, ama-GPU, kanye nama-FPGA imbala.
Lokhu kukwenza kube enye indlela enhle kakhulu yezinhlangano ezizama ukuhlanganisa ukufunda okujulile ezimpahleni nasezinsizeni zazo. Ngaphezu kwalokho, i-Cognitive Toolkit ihlanganisa izinhlobonhlobo zamamodeli akhiwe kusengaphambili kanye nekhodi yesibonelo, okwenza kube lula kwabasanda kungena ukuqalisa.
buhle
- Ivumela ukuqeqeshwa okusabalalisiwe kumakhompuyutha amaningana nama-GPU
- Ihlinzeka ngokusebenzisana okulula neminye imikhiqizo ye-Microsoft efana ne-Azure kanye ne-Power BI
- Inikeza uhlaka oluguquguqukayo noluguquguqukayo lokuthuthukisa nokuqeqesha amamodeli okufunda ajulile
bawo
- Kungase kube nzima ukusetha nokwenza ngendlela oyifisayo kubasebenzisi abasha
- Intula usekelo olwakhelwe ngaphakathi lwezici ezimbalwa ezidumile ezifana nokwengezwa kwedatha nokufunda kokudlulisa
- Intula usekelo olwakhelwe ngaphakathi lwezici ezimbalwa ezidumile ezifana nokwengezwa kwedatha nokufunda kokudlulisa
8. Shogun
I-Shogun iyiphakheji yokufunda yomshini ye-C++ engasetshenziswa kancane. Iqukethe izixhumi ze-Python, i-Java, ne-MATLAB, okuyenza ibe ithuluzi elivumelana nezimo labasebenzi bokufunda ngomshini.
I-Shogun yakhelwe ukuthi ikwazi ukukala, isheshe, futhi ivumelane nezimo, iyenze ifanele inani elikhulu ledatha kanye nemithwalo yemisebenzi yokufunda yomshini eyinselele.
Enye yezinzuzo eziphawuleka kakhulu zika-Shogun amandla akhe okuphatha amafomethi wedatha anhlobonhlobo, okuhlanganisa kanambambili, ngokwezigaba, naqhubekayo.
Iphinde ihlanganise nezinhlobonhlobo zezindlela zokuhlukanisa, ukuhlehla, ukuncishiswa kobukhulu, nokuhlanganisa, okulenza libe ithuluzi lokufunda lomshini eliphelele. I-Shogun isekela kokubili iqoqo nokufunda ku-inthanethi, futhi ihlanganisa ngaphandle komthungo namanye amalabhulali okufunda omshini afana ne-TensorFlow kanye ne-scikit-learn.
buhle
- Ihlinzeka ngesethi ehlukahlukene yamasu namathuluzi okufunda ngomshini, okuhlanganisa ukufunda okujulile, ukwehla, nokusekelwa ngezigaba.
- Iyahambisana nezinhlobonhlobo ze izilimi zokuhlela, okuhlanganisa iPython, C++, neJava.
bawo
- Ingase ibe nezinsiza ezimbalwa nokusekelwa okutholakalayo njengoba ingase ingaziwa kakhulu noma idume njengamanye amalabhulali okufunda omshini.
- Uma kuqhathaniswa neminye imitapo yolwazi abayijwayele, abanye abasebenzisi bangathola i-syntax nesakhiwo salo mtapo kungaqondakali kahle.
- Ukuze kutholwe imiphumela engcono kakhulu, imitapo yolwazi ethile ingase idinge umsebenzi wezandla nokuhlelwa kahle kuneminye.
9. I-ONNX
Inkundla yomthombo ovulekile ebizwa nge-Open Neural Network Exchange (ONNX) inika amandla ukuguqulwa nokwabelana kwamamodeli okufunda omshini.
Inikeza indlela yokudlulisa amamodeli okufunda ajulile phakathi kwezinhlaka ezihlukahlukene nezinkundla, ukwenza kube lula ukudalwa nokusetshenziswa kwamamodeli okufunda omshini.
Ungakha amamodeli nge-ONNX usebenzisa uhlaka oluncanyelwayo bese uwasebenzisa esimisweni sesikhathi sokusebenza esihlukile.
Izakhiwo ezenziwe ngendlela oyifisayo ze-ONNX zenza abasebenzisi bakwazi ukukhetha amathuluzi afanele omsebenzi owenziwayo. Isiza ukuhambisana kuzo zonke izinhlaka zokufunda ezijulile ezimbalwa, njenge-PyTorch, i-TensorFlow, ne-Caffe2. Ungasebenzisa izinzuzo zohlaka ngalunye ngokuguqula ngokushesha amamodeli phakathi kwawo.
buhle
- Ukusebenzisana kungenzeka kuzo zonke izinhlaka zokufunda ezijulile.
- Kumahhala ukusebenzisa nomthombo ovulekile.
- Kusekelwa uhla olubanzi lwezindawo zehadiwe nesikhathi sokusebenza.
bawo
- Ukusebenza kwamamodeli we-ONNX ngezinye izikhathi kungaba kubi kakhulu kunamamodeli asetshenziswa ngokomdabu kuhlaka olunikeziwe.
- Kwesinye isikhathi ukushintsha phakathi kwezinhlaka ezahlukene kungase kubangele izinkinga zokusebenzisana okunzima ukuzilungisa.
10. I-Apache Spark
I-Apache Spark iwuhlelo lwekhompuyutha olusabalalisiwe olusheshayo futhi oluguquguqukayo olungaphatha kalula ukucutshungulwa kwedatha enkulu. Kuyinketho edumile yezinhlelo zokusebenza ezinkulu zedatha ngenxa yekhono layo lokuhlaziya amanani amakhulu wedatha ngokushesha.
I-Spark ayihloselwe ukuthi isheshe nje kuphela, kodwa futhi iyalinganiswa, okusho ukuthi ingaphatha amanani edatha akhulayo ngaphandle kokuphazamisa ukusebenza.
Iphakethe le-MLlib elifakwe ne-Apache Spark liphawuleka kakhulu. Kuhlanganisa izindlela zokufunda zomshini ezingaka neziphumelelayo ezifana nokuhlukanisa, ukuhlehla, ukuhlanganisa, nokuhlunga ngokubambisana.
Ngenxa yokuthi i-MLlib ihlangana nezinye izingxenye ze-Spark, kulula ukwakha amapayipi okucubungula idatha asuka ekupheleni aye ekugcineni.
Ngakho-ke, uma udinga ithuluzi eliqinile neliguquguqukayo lokucubungula idatha enkulu nokufunda ngomshini, i-Apache Spark kufanele ibe sohlwini lwakho.
buhle
- Ngenxa yomklamo wayo wekhompuyutha osabalalisiwe, ingakwazi ukuphatha amadathasethi amakhulu ngokushesha
- Ukuhlanganiswa nobunye ubuchwepheshe beDatha Enkulu njengeHadoop, iHive, neCassandra kulula.
- Kunikezwe izindlela ezimbalwa zokuhlukanisa, ukuhlehla, ukuhlanganisa, nokuhlunga ngokubambisana
bawo
- Ngenxa yobunkimbinkimbi bezakhiwo zekhompuyutha esabalalisiwe, ijika lokufunda liyakhuphuka
- Isebenza ngesamba esikhulu sezinsiza nengqalasizinda
- Ukusekela ukucubungula kwesikhathi sangempela nokusakaza idatha kunqunyelwe
11. mpack
I-mlpack iwumthombo ovulekile wekhithi yokufunda yomshini ye-C++ ehloselwe ukuhlinzeka ngama-algorithms asheshayo, alinganisekayo, nalula ezinhlobonhlobo zezinhlelo zokusebenza.
Ihlinzeka ngesethi ehlukahlukene yama-algorithms okufunda komshini njengokuhlanganisa, ukuhlehla, ukuhlukanisa, ukuncishiswa kobukhulu, namanethiwekhi emizwa.
buhle
- Ukuqaliswa okusebenzayo kwama-algorithms amaningi
- Ukuhlanganisa neminye imitapo yolwazi nezilimi kulula.
- Inikeza umugqa womyalo kanye ne-C++ API interface
bawo
- Amadokhumenti angathuthukiswa
- Ama-algorithms ambalwa awakasetshenziswa
- Abaqalayo bangase bakuthole kunzima ukukusebenzisa
12. I-Azure ML Studio
I-Azure Machine Learning (Azure ML) iyinkundla yokufunda yomshini emafini. Uyakwazi ukuklama, ukuphakela, nokuphatha amamodeli okufunda omshini esikalini.
Inikeza amathuluzi namasevisi ahlukahlukene ukusiza ososayensi bedatha nabathuthukisi ekwenzeni kahle ukuhamba komsebenzi wokufunda komshini ekupheleni uye ekupheleni. Ungamane uphathe idatha yakho, uqeqeshe amamodeli akho, futhi uwathumele ekukhiqizeni. Futhi ungakwazi ukuqapha ukusebenza kwabo usebenzisa i-Azure ML—konke kusuka endaweni eyodwa ehlanganisiwe.
Inkundla isekela izilimi zekhompuyutha ezimbalwa, okuhlanganisa i-Python, i-R, ne-SQL, futhi iza nezifanekiso ezimbalwa ezakhiwe ngaphambilini nama-algorithms okukusiza ukuthi uqalise ngokushesha.
Ngaphezu kwalokho, ngenxa yedizayini yayo eguquguqukayo nenwebekayo, i-Azure ML ingakwazi ukuphatha kalula kokubili izivivinyo ezincane kanye nezicelo zokufunda zomshini ezinkulu.
buhle
- Inikeza isixhumi esibonakalayo esicacile esisebenziseka kalula sokuthuthukisa nokuphakela amamodeli okufunda omshini
- Ixhuma kwezinye izinsiza ze-Microsoft njenge-Azure Storage kanye ne-Power BI.
- Ukusebenzisana namalungu eqembu kungenzeka ngokulawula inguqulo nezindawo zokusebenza ezabiwe
- I-Scalability yokubhekana namavolumu amakhulu wedatha namandla okucubungula
bawo
- Izinketho ezincane zokwenza ngokwezifiso zama-algorithms namamodeli
- Ngenxa yesu lentengo, ingase ingabizi kangako emabhizinisini amancane noma kubantu ngabanye
13. I-Sonnet
Abacwaningi be-DeepMind baklame futhi bakhe i-Sonnet, uhlaka lwe-AI olusekela ukuthuthukiswa kwamanethiwekhi e-neural ezinhlelo zokusebenza ezihlukahlukene. Lokhu kubandakanya ukufunda okugadiwe nokungagadiwe, kanjalo ukuqinisa ukufunda.
I-Sonnet's programming architecture yakhelwe ku-snt.Module, engagcina izikhombi kumapharamitha, amanye amamojula, nezindlela. Uhlaka luza namamojula amaningana asethiwe ngaphambilini namanethiwekhi, kodwa abasebenzisi nabo bayakhuthazwa ukuthi benze awabo.
buhle
- Imodeli yokuhlela elula nenamandla
- Abasebenzisi bayakhuthazwa ukuthi benze amamojula abo.
- Ikhodi emfushane futhi egxile
bawo
- Alukho uhlelo lokuqeqesha olufakiwe
- Abasaqalayo bangase babhekane nejika lokufunda eliwumqansa
14. I-GluonCV
Ingabe uyafuna ukufunda okwengeziwe mayelana nombono wekhompyutha?
Sethula i-GluonCV!
Lo mtapo wezincwadi omuhle uqukethe ama-algorithms wokufunda ajulile, amamodeli aqeqeshwe ngaphambilini, kanye nenqwaba yezinto zokusiza onjiniyela, abacwaningi, nabafundi ekuqinisekiseni imibono yabo, imikhiqizo ye-prototyping, nokufunda okwengeziwe mayelana nendawo.
I-GluonCV ikwenza kube lula ukuqalisa nokuzuza imiphumela ye-SOTA ngama-API ayo aklanywe kahle, ukusetshenziswa okulula, nosizo lomphakathi.
Yini enye, ingxenye enhle kakhulu?
Ivumelana nezimo ngokwedlulele futhi kulula ukuyisebenzisa nokuyifaka! I-GluonCV iqukethe konke okudingayo ukuze uthathe amathalente okubona kwikhompyutha yakho uwayise ezingeni elilandelayo, noma ngabe unguchwepheshe noma usaqala.
buhle
- Ukufakwa nokusetshenziswa okulula
- Iqoqo elikhulu lamamodeli aqeqeshwe ngaphambilini
- Ama-algorithms wokufunda okujulile ezisezingeni eliphezulu
- Ukuqaliswa okulula ukukuqonda
- Ukwenza ngcono nokusabalalisa okulula
bawo
- Ukwenza ngokwezifiso nokulawula okuncane kunezinye izinhlaka
- Ukwesekwa kwemisebenzi yokubona okungeyona eyekhompyutha kunqunyelwe
- Ukusetshenziswa kwezentengiso kungase kukhawulelwe ngenxa yemikhawulo yelayisensi
15. H2O
I-H2O iwumthombo ovulekile wokuhlaziya idatha kanye nenkundla yokufunda yomshini ehlose ukwenza kube lula ukuthi izinhlangano zisebenzise ubuhlakani bokwenziwa (AI) ukushayela imisebenzi yazo.
I-H2O.ai's AI Cloud yenza ukuqalisa nge-H2O kube lula nakakhulu, ngesixhumi esibonakalayo sokudonsa nokuwisa sokuthuthukisa amamodeli okufunda omshini ngaphandle kwamakhono okubhala amakhodi.
Ipulatifomu iphinde inikeze okubanzi ukubona ngemininingwane kanye namakhono okuhlaziya, kanye nokulungisa amamodeli nokusatshalaliswa. Amabhizinisi angasebenzisa i-H2O.ai ukuze akhe ngokushesha futhi kalula futhi akhiphe amamodeli e-AI ukuze abhekane nezinselele zebhizinisi eziyinselele.
buhle
- Hudula bese udedela isixhumi esibonakalayo sokudala amamodeli okufunda omshini
- Amathuluzi okubona ngeso wedatha aphelele namathuluzi okuhlaziya, kanye nokuhlela amamodeli nokusetshenziswa
- Inkundla yomthombo ovulekile enomsebenzisi omkhulu nomphakathi wabanikeli
- Ukusekelwa kwama-algorithms ambalwa nezinhlobo zedatha
bawo
- Izici ezithile zifinyeleleka kuphela enguqulweni ye-premium yesikhulumi
- Uma kuqhathaniswa nezinye izinkundla, kungase kube nzima kakhulu ukusetha nokulungisa.
Ukuphetha, Iyiphi Engcono Kakhulu?
Ukukhetha uhlaka lwe-AI olufanele noma inkundla kuya ngokuthi ufuna ukwenzani ngayo. Uma ufuna uhlaka olulula ukulusebenzisa futhi olunomphakathi omkhulu, i-TensorFlow noma i-PyTorch ingaba inketho efanelekile.
Uma ufuna inkundla egxile kakhulu kumamodeli okufunda omshini, i-Azure ML Studio noma i-H2O.ai ingase ibe inketho engcono kakhulu.
Futhi, uma ufuna uhlaka olulula ukwenza ngendlela oyifisayo nokulumisa, i-Sonnet noma i-GluonCV ingase ibe indlela okufanele uhambe ngayo. Okokugcina, uhlaka olufanele kuwe lunqunywa izimfuno zakho ezihlukile kanye nokuthandayo.
shiya impendulo