Ynsaghey jeshaghtagh
She enmys ymlane eh '''ynsaghey jeshaghtagh''' ta cur sheese er feaysley feyshtyn. Son eh shen dy yannoo, veagh algorithmyn lhiassit ec claareyderyn deiney ro-gheyr as, ayns e ynnyd ta ny feyshtyn feayslit erreish da cooney y chur rish jeshaghtyn nyn algorithmyn 'hene' y 'gheddyn magh',<ref name = "Alpaydin2020">{{cite book | author = Ethem Alpaydin | title = Introduction to Machine Learning | year = 2020 | edition = Fourth | pages = xix, 1–3, 13–18 | publisher = [[MIT]] |isbn = 978-0262043793}}</ref> gyn coyrle jarroo voish un algorithm va lhassit ec deiney. Er gherrid, haink er [[far-voggyl neayragh|far-voggylyn neayragh]] gientynagh dy ve jargal goll harrish troaryn ny h-aghtyn hie rouesyn.<ref name="ibm">{{Cite web |title=What is Machine Learning? {{!}} IBM |url=https://www.ibm.com/topics/machine-learning |accessdate=2023-06-27 |website=www.ibm.com |language=en}}</ref><ref name=":6">{{Cite web |last=Zhou |first=Victor |date=2019-12-20 |title=Machine Learning for Beginners: An Introduction to Neural Networks |url=https://towardsdatascience.com/machine-learning-for-beginners-an-introduction-to-neural-networks-d49f22d238f9 |url-status=live |accessdate=2021-08-15 |website=Medium |language=en |archivedate=2022-03-09 |archiveurl=https://web.archive.org/web/20220309053518/https://towardsdatascience.com/machine-learning-for-beginners-an-introduction-to-neural-networks-d49f22d238f9 }}</ref> Ta aghtyn yn ynsaghey jeshaghtagh er ny ve currit rish [[mooar-chlowan glare|mooar-chlowanyn glare]], [[feesh cho-earrooagh]], [[enney glare]], [[sheelaghey post lectraneagh]], [[eirinys]] as lheihys, raad t'eh ro-gheyr algorithmyn y lhiassaghey son dy yannoo kiartagyn enmyssit as shirrit.<ref name="tvt">{{Cite journal |last1=Hu |first1=Junyan |last2=Niu |first2=Hanlin |last3=Carrasco |first3=Joaquin |last4=Lennox |first4=Barry |last5=Arvin |first5=Farshad |date=2020 |title=Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning |url=http://dx.doi.org/10.1109/tvt.2020.3034800 |journal=IEEE Transactions on Vehicular Technology |volume=69 |issue=12 |pages=14413–14423 |doi=10.1109/tvt.2020.3034800 |s2cid=228989788 |issn=0018-9545 |accessdate=2023-04-16 |archivedate=2023-04-16 |archiveurl=https://web.archive.org/web/20230416160303/https://ieeexplore.ieee.org/document/9244647/ |url-status=live }}</ref><ref name=":7">{{cite journal |last1=Yoosefzadeh-Najafabadi|first1=Mohsen |last2=Hugh |first2=Earl |last3=Tulpan |first3=Dan |last4=Sulik |first4=John |last5=Eskandari |first5=Milad |title=Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean? |journal=Front. Plant Sci. |volume=11 |year=2021 |pages=624273|doi=10.3389/fpls.2020.624273 |pmid=33510761 |pmc=7835636 |doi-access=free }}</ref>

Ta bun maddaghtoil yn ynsaghey jeshaghtagh kiarit trooid aghtyn y [[lhiassaghey maddaghtoil]] (claaraghey maddaghtoil). She keird co-chianglt ee [[meaineraght fysseree]], ta jeeraghey er [[oltscarrey fysseree ronsoil]] trooid [[ynsaghey neu-oaseirit]].<ref>{{cite journal |last=Friedman |first=Jerome H. |author-link = Jerome H. Friedman|title=Data Mining and Statistics: What's the connection? |journal=Computing Science and Statistics |volume=29 |issue=1 |year=1998 |pages=3–9}}</ref> 

Ta'n ynsaghey maddaghtoil goll er enney 'syn ymmyd jeh ayns feyshtyn dellal fo'n ennym [[olt-scarreydaght aaishnagh]]. Ga nagh vel dagh ynsaghey jeshaghtagh bunnit er y staddyssaght, she bun scanshoil aghtyn y cheird ee [[staddyssaght cho-earrooagh]]. 

==Imraaghyn==
{{rolleyimraaghyn}}

==Kianglaghyn magh==
{{Commons category|Machine learning|Ynsaghey jeshaghtagh}}
*[https://web.archive.org/web/20171230081341/http://machinelearning.org/ International Machine Learning Society]
*[https://mloss.org/ mloss] stoyr-fysseree jeh cooid vog heyr oshlit.

[[Ronney:Ynsaghey jeshaghtagh| ]]
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[[Ronney:Ynsaghey|Jeshaghtagh]]
She enmys ymlane eh ynsaghey jeshaghtagh ta cur sheese er feaysley feyshtyn. Son eh shen dy yannoo, veagh algorithmyn lhiassit ec claareyderyn deiney ro-gheyr as, ayns e ynnyd ta ny feyshtyn feayslit erreish da cooney y chur rish jeshaghtyn nyn algorithmyn 'hene' y 'gheddyn magh', gyn coyrle jarroo voish un algorithm va lhassit ec deiney. Er gherrid, haink er far-voggylyn neayragh gientynagh dy ve jargal goll harrish troaryn ny h-aghtyn hie rouesyn. Ta aghtyn yn ynsaghey jeshaghtagh er ny ve currit rish mooar-chlowanyn glare, feesh cho-earrooagh, enney glare, sheelaghey post lectraneagh, eirinys as lheihys, raad t'eh ro-gheyr algorithmyn y lhiassaghey son dy yannoo kiartagyn enmyssit as shirrit.

Ta bun maddaghtoil yn ynsaghey jeshaghtagh kiarit trooid aghtyn y lhiassaghey maddaghtoil (claaraghey maddaghtoil). She keird co-chianglt ee meaineraght fysseree, ta jeeraghey er oltscarrey fysseree ronsoil trooid ynsaghey neu-oaseirit. 

Ta'n ynsaghey maddaghtoil goll er enney 'syn ymmyd jeh ayns feyshtyn dellal fo'n ennym olt-scarreydaght aaishnagh. Ga nagh vel dagh ynsaghey jeshaghtagh bunnit er y staddyssaght, she bun scanshoil aghtyn y cheird ee staddyssaght cho-earrooagh. 

Imraaghyn

Kianglaghyn magh

International Machine Learning Society
mloss stoyr-fysseree jeh cooid vog heyr oshlit.

 

Jeshaghtagh