Machine Kudzidza: Iyo Artificial Intelligence Revolution

Machine Kudzidza (ML) imunda weArtificial Intelligence (AI) iyo yakatsaurirwa kugadzira maalgorithms ayo anogona kudzidza otomatiki kubva kune data pasina kurongeka zvakajeka. Ndiyo imwe yenzvimbo dzinovimbisa zvikuru dzeAI uye iri kuita zvakakosha pamaindasitiri akasiyana siyana, akadai sehutano, mari, zvekufambisa nekutengesa.

Kune marudzi maviri makuru eML: Kudzidza Kunotariswa uye Kudzidza Kusina Kutariswa. Mukudzidza kunotariswa, algorithm inopiwa seti yedata rakanyorwa, kureva, data ine mhinduro dzakakodzera. Iyo algorithm inodzidza kubatanidza izvo zvinopinda neizvo zvinobuda. Mukudzidza kusina kutariswa, iyo algorithm haina mavara. Iwe unofanirwa kudzidza kutsvaga mapatani mune data iwe pachako.

Mamwe eanonyanya kufarirwa Machine Kudzidza algorithms ndeaya:

• Linear regression
• Muti wesarudzo
• Neural network
• Vector rutsigiro muchina

Aya maalgorithms anogona kushandiswa kugadzirisa matambudziko mazhinji, akadai se:

• Kurongwa
• Kudzvinyirira
• Batch kuronga
• kudzidza kwakadzama

Ichi chishandiso chine simba chine mukana wekushandura maindasitiri mazhinji. Sezvo ML algorithms ichiwedzera kuwanda, isu tinogona kutarisira kuona zvakatonyanya kuvandudza maapplication mune ramangwana.

Kudzidza kweMichina kunoshanda sei?

ML inoshanda nekushandisa data kudzidzisa algorithm. Iyo algorithm inodzidza kubatanidza zvinopinda nezvinobuda kubva kune data. Kana iyo algorithm ichinge yadzidziswa, inogona kushandiswa kufanotaura pane nyowani data.

Semuenzaniso, Machine Learning algorithm inogona kudzidziswa kuziva katsi mumifananidzo. Iyo algorithm yaizodzidziswa nedataset yekatsi uye isiri-katsi mifananidzo. Iyo algorithm yaizodzidza kuziva maficha emifananidzo yekatsi, senge chimiro chemusoro, maziso uye muswe. Kana iyo algorithm ichinge yadzidziswa, inogona kushandiswa kuona katsi mumifananidzo mitsva.

Ndezvipi zvakanakira Machine Kudzidza?

Mabhenefiti akawanda. Zvimwe zvezvakakosha zvinobatsira zvinosanganisira:

• Automation: ML inogona kuita mabasa mazhinji arikuitwa nevanhu parizvino. Izvi zvinogona kusunungura nguva uye zviwanikwa kuti vanhu vatarise pane mamwe mabasa ehurongwa.

• Kurongeka: ML inogona kunyatsojeka kupfuura nzira dzekuongorora dzechinyakare. Izvi zvinodaro nekuti Kudzidza Kwemuchina kunogona kudzidza kubva kune data uye kugadzirisa fungidziro dzayo zvichienderana nedata idzva.

• Kunyatsogona: ML inogona kunyatsoshanda kupfuura nzira dzekuongorora dzechinyakare. Izvi zvinodaro nekuti Kudzidza Kwemuchina kunogona kugadzirisa huwandu hukuru hwe data nekukurumidza uye nemazvo.

• Innovation: ML inogona kubatsira kuburitsa mazano matsva uye hunyanzvi. Izvi zvinodaro nekuti Kudzidza kweMichina kunogona kudzidza kubva kune data uye kuwana mapatani ayo vanhu vasingaone.

Ndeapi matambudziko eMuchina Kudzidza?

Matambudziko eMuchina Kudzidza zvakare akawanda. Mamwe ematambudziko akanyanya kukosha anosanganisira:

• Kuwanikwa kwedata: Kudzidza kunoda huwandu hukuru hwe data kudzidzisa maalgorithms. Zvinogona kunetsa kuwana iyo data inodiwa, kunyanya kana iyo data yakavanzika kana copyrighted.

• Iyo yakaoma yedata: Dhata inogona kuve yakaoma uye yakaoma kuongorora. Izvi zvinogona kuita kuti zviome kudzidzisa zvakarurama MLearning algorithms.

• Kududzira zvawawana: Zvawawana zvinogona kunetsa kududzira. Izvi zvinodaro nekuti MLearning algorithms inogona kudzidza mapatani ayo vanhu vasingaone.

Kunyangwe zvipingamupinyi, ML tekinoroji ine mukana mukuru wekuita zvakanaka pasirese. Sezvo Machine Kudzidza algorithms achiwedzera kuomarara, isu tinogona kutarisira kuona zvakatonyanya kuvandudza maapplication mune ramangwana.

Ndingaite sei kuti ndidzidze zvakawanda nezve Machine Kudzidza?

Kana iwe uchida kudzidza zvakawanda pamusoro peizvi, pane zvakawanda zvekushandisa zviripo. Unogona kuwana mabhuku, zvinyorwa, online makosi uye tutorials. Iwe unogona zvakare kuwana nharaunda dzevashandisi uye maforamu kwaunogona kudzidza kubva kune vamwe vanofarira Machine Kudzidza.

Kana iwe uchida kudzidza zvakawanda, tinokurudzira kutanga nekutanga. Dzidza nezvemhando dzakasiyana dzeMachina Kudzidza algorithms, mashandiro avanoita, uye mashandisiro avanogona kugadzirisa matambudziko. Paunenge uchinge wava nekunzwisisa kwekutanga kweiyo yekutanga, unogona kutanga kudzidza nezve mamwe chaiwo maapplication.

Ndeapi marudzi akasiyana eKudzidza kweMichina?

Kune marudzi maviri makuru eKudzidza kweMichina: Kudzidza Kunotariswa uye Kudzidza Kusina Kutariswa.

Yakarongedzwa Kudzidza

Mukudzidza kunotariswa, algorithm inopiwa seti yedata rakanyorwa, kureva, data ine mhinduro dzakakodzera. Iyo algorithm inodzidza kubatanidza izvo zvinopinda neizvo zvinobuda.

Kudzidzira Kusina Kutarisirwa

Mukudzidza kusina kutariswa, iyo algorithm haina mavara. Iwe unofanirwa kudzidza kutsvaga mapatani mune data iwe pachako. Semuenzaniso, algorithm yekudzidza isina kutariswa inogona kudzidziswa kuisa vatengi mumapoka akasiyana. Iyo algorithm yaizodzidza kutsvaga mapatani mune data revatengi, sezera ravo, mari, uye nzvimbo. Kana iyo algorithm ichinge yadzidziswa, inogona kushandiswa kuunganidza vatengi vatsva muzvikamu zvakafanana.

Ndeapi mamwe emashandisirwo eMuchina Kudzidza?

ML inoshandiswa mune dzakasiyana siyana maindasitiri, akadai sehutano, mari, zvekufambisa, uye zvitoro. Mamwe eanonyanya kufarirwa maapplication anosanganisira:

• Kurongwa: M Kudzidza inogona kushandiswa kuisa data muzvikamu zvakasiyana. Semuenzaniso, Machine Learning algorithm inogona kushandiswa kurongedza mifananidzo yekatsi nembwa.
• Kudzvinyirira: M Kudzidza kunogona kushandiswa kufanotaura zvinoramba zvichikosha. Semuenzaniso, Machine Learning algorithm inogona kushandiswa kufanotaura mutengo westock kana mukana wekuti mutengi angaite.
• Grouping: M Kudzidza kunogona kushandiswa kuisa data mumapoka akasiyana. Semuenzaniso, Machine Kudzidza algorithm inogona kushandiswa kuisa vatengi mumapoka akasiyana zvichienderana nehunhu hwavo.
• kudzidza kwakadzama: Kudzidza kweMichina kunogona kushandiswa kugadzira mhando dzinokwanisa kudzidza kubva kune yakawanda data. Semuenzaniso, algorithm yekudzidza yakadzama inogona kushandiswa kuona mifananidzo yegomarara rezamu mune mammograms.

Ndeapi mamwe eMLearning maitiro emangwana?

Mamwe eMachina Kudzidza maitiro emangwana anosanganisira:

• Kuwedzera kwekushandiswa kwemashoko makuru: Huwandu hukuru hwe data hunodiwa kudzidzisa maalgorithms. Sezvo nyika inowedzera dhijitari, data rakawanda rinogadzirwa. Izvi zviri kugadzira mikana mitsva yekushandiswa kwayo.

• Kugadziriswa kwealgorithms nyowani: Vatsvagiri vari kugara vachigadzira matsva eMashini Kudzidza algorithms. Aya maalgorithms matsva ane chokwadi uye anoshanda kupfuura algorithms apfuura.

• Su kushandiswa muminda mitsva: Kudzidza kweMichina kuri kushandiswa munzvimbo dzakasiyana siyana, dzakadai sehutano, mari, chekufambisa, uye kutengesa. Sezvo tekinoroji ichiwedzera kuwanda, tinogona kutarisira kuona kushandiswa kwayo muminda mitsva.

MLearning tekinoroji ine simba ine mukana wekushandura maindasitiri mazhinji. Sezvo aya maalgorithms achiwedzera kuomarara, isu tinogona kutarisira kuona mamwe maapplication matsva mune ramangwana.